commit 0a55543ae26acaf69f069d1f795d6e17f4bcb1b5
Author: xausssr <xausssr@gmail.com>
Date:   Sat May 11 12:29:10 2024 +0300

    init commit: загрузка и предобработка данных

diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..5f7d16d
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,6 @@
+Записи/OpenBCI GUI
+Записи/OpenVibe Signals
+__pycache__
+.ipynb_checkpoints
+.pytest_cache
+runs
\ No newline at end of file
diff --git a/LUA/motor-imagery-bci-epoch-selector.lua b/LUA/motor-imagery-bci-epoch-selector.lua
new file mode 100644
index 0000000..3959368
--- /dev/null
+++ b/LUA/motor-imagery-bci-epoch-selector.lua
@@ -0,0 +1,50 @@
+
+g_offset = nil
+g_duration = nil
+
+-- this function is called when the box is initialized
+function initialize(box)
+
+	dofile(box:get_config("${Path_Data}") .. "/plugins/stimulation/lua-stimulator-stim-codes.lua")
+
+	g_offset = box:get_setting(2)
+	g_duration = box:get_setting(3)
+end
+
+-- this function is called when the box is uninitialized
+function uninitialize(box)
+       
+end
+
+function wait_until(box, time)
+  while box:get_current_time() < time do
+    box:sleep()
+  end
+end
+
+
+function wait_for(box, duration)
+  wait_until(box, box:get_current_time() + duration)
+end
+
+
+
+function process(box)
+	-- loops on every received stimulation for a given input
+	while box:keep_processing() do
+		for stimulation = 1, box:get_stimulation_count(1) do
+
+				-- gets the received stimulation
+				identifier, date, duration = box:get_stimulation(1, 1)
+				-- discards it
+				box:remove_stimulation(1, 1)
+				
+				-- delay the OVTK_GDF_Left and Right
+				if identifier == OVTK_GDF_Left or identifier == OVTK_GDF_Right then
+					box:send_stimulation(1, OVTK_GDF_Correct, date+g_offset, 0)
+					box:send_stimulation(1, OVTK_GDF_Incorrect, date+g_offset+g_duration, 0)
+				end
+		end
+		box:sleep()
+	end
+end
diff --git a/baseline_classifier.ipynb b/baseline_classifier.ipynb
new file mode 100644
index 0000000..838cfaf
--- /dev/null
+++ b/baseline_classifier.ipynb
@@ -0,0 +1,471 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import torch\n",
+    "\n",
+    "from pathlib import Path\n",
+    "\n",
+    "from matplotlib import pylab as plt\n",
+    "\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import balanced_accuracy_score\n",
+    "\n",
+    "import pytorch_lightning as pl\n",
+    "from pytorch_lightning.callbacks import ModelCheckpoint\n",
+    "\n",
+    "from core.io import filter_events, get_ranges, split_epochs_binary\n",
+    "from core.signal_processing import filter_signal, __create_constants, CSPFilter\n",
+    "from core.nn.provider import NetProvider\n",
+    "from core.nn.data import TableDataset\n",
+    "\n",
+    "%matplotlib inline\n",
+    "plt.style.use('bmh')\n",
+    "plt.rcParams.update({'font.size': 14})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Необходимо проверить точность классификации с помощью полносвязной сети:\n",
+    "\n",
+    "1. Подобрать архитектуру (поиск по сетке) - возможно полносвязные слои заменить на свёртку\n",
+    "2. Выбрать электроды\n",
+    "3. Исследовать влияние фильтрации\n",
+    "4. Исследовать влияние использования CSP-фильтра\n",
+    "\n",
+    "> По числовым значениям - **обсудить с Андреем Николаевичем**\n",
+    "\n",
+    "> **Окружение TacticENV**\n",
+    "\n",
+    "> **Сохранять все результаты - ребятам понадабятся графики/таблицы в статье, необзодимые визуализации обсудить с Андреем Николаевичем**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Примеры использования библеотеки `core`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Данные"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Загрузка"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "openvibe_config = json.load(open(\"./config/openVibe.json\", \"r\"))\n",
+    "raw_data = pd.read_csv(\"./Записи/OpenVibe signals/motor-imagery-csp-1-acquisition-[2024.04.18-10.37.21].csv\")\n",
+    "data = filter_events(raw_data, openvibe_config)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Фильтрация"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "constants = __create_constants(low=8.0, high=30.0, sample_rate=250.0, order=5) \n",
+    "filtered_data = filter_signal(df=data, cols=[x for x in data.columns if \"Channel\" in x], constants=constants)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Получение отрезков и разметки"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = split_epochs_binary(\n",
+    "    df=filtered_data, \n",
+    "    epoch_duration=4 * 250, \n",
+    "    init_pause=int(0.5 * 250), \n",
+    "    final_pause=0, \n",
+    "    step=50000, # берем только 1 отрезок на действие (обсудить с А.Н.), подробнее см. доку \n",
+    "    map_labels={\"left\": 0, \"right\": 1},\n",
+    "    cols=[x for x in data.columns if \"Channel\" in x]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### CSP-фильтр"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(40, 1000)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "csp_filter = CSPFilter(4)\n",
+    "csp_filter.fit(x, y)\n",
+    "csp_filter.transform(x).shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Нейронки\n",
+    "\n",
+    "> Используем `pytorch-lightning`, если будешь добавлять - придерживайся стиля 🤖\n",
+    "\n",
+    "Ниже пример полного пайплайна обучения (без фильтрации, без сколбзящего окна, без CSP-фильтра) для 3 случайных электродов - `Channel 1`, `Channel 2`, `Channel 3`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(40, 3000) (40, 2)\n"
+     ]
+    }
+   ],
+   "source": [
+    "raw_data = pd.read_csv(\"./Записи/OpenVibe signals/motor-imagery-csp-1-acquisition-[2024.04.18-10.37.21].csv\")\n",
+    "data = filter_events(raw_data, openvibe_config)\n",
+    "x, y = split_epochs_binary(\n",
+    "    df=data, \n",
+    "    epoch_duration=4 * 250, \n",
+    "    init_pause=int(0.5 * 250), \n",
+    "    final_pause=0, \n",
+    "    step=50000, # берем только 1 отрезок на действие (обсудить с А.Н.), подробнее см. доку \n",
+    "    map_labels={\"left\": 0, \"right\": 1},\n",
+    "    cols=[\"Channel 1\", \"Channel 2\", \"Channel 3\"]\n",
+    ")\n",
+    "\n",
+    "# Делаем сигналы плоскими, лейблы в OneHot\n",
+    "x = x.reshape((-1, 3 * 1000))\n",
+    "y =  np.eye(2)[y]\n",
+    "print(x.shape, y.shape)\n",
+    "\n",
+    "# бъем train/test\n",
+    "X_train, X_test, y_train, y_test = train_test_split(x, y, train_size=0.8)\n",
+    "\n",
+    "# конвертим в объекты торча\n",
+    "train_loader = torch.utils.data.DataLoader(TableDataset(X_train, y_train), batch_size=32)\n",
+    "val_loader = torch.utils.data.DataLoader(TableDataset(X_test, y_test), batch_size=8)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "net = torch.nn.Sequential(\n",
+    "    torch.nn.Linear(3000, 1024),\n",
+    "    torch.nn.Sigmoid(),\n",
+    "    torch.nn.Linear(1024, 2),\n",
+    "    torch.nn.Softmax(dim=1),\n",
+    ")\n",
+    "\n",
+    "wrapped_net = NetProvider(\n",
+    "    net=net, \n",
+    "    lr=1e-4, \n",
+    "    criteria=torch.nn.BCELoss(), \n",
+    "    metrics=[\n",
+    "        lambda pred, labels: balanced_accuracy_score(np.argmax(labels, axis=1), np.argmax(pred, axis=1))\n",
+    "    ],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "GPU available: True (cuda), used: True\n",
+      "TPU available: False, using: 0 TPU cores\n",
+      "IPU available: False, using: 0 IPUs\n",
+      "HPU available: False, using: 0 HPUs\n",
+      "Missing logger folder: runs\\example\\lightning_logs\n",
+      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
+      "\n",
+      "  | Name     | Type       | Params\n",
+      "----------------------------------------\n",
+      "0 | criteria | BCELoss    | 0     \n",
+      "1 | net      | Sequential | 3.1 M \n",
+      "----------------------------------------\n",
+      "3.1 M     Trainable params\n",
+      "0         Non-trainable params\n",
+      "3.1 M     Total params\n",
+      "12.300    Total estimated model params size (MB)\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dc2a3deccc5b4690a005b7309e3f9322",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Sanity Checking: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\user\\anaconda3\\envs\\TacticENV\\lib\\site-packages\\pytorch_lightning\\trainer\\connectors\\data_connector.py:441: The 'val_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=11` in the `DataLoader` to improve performance.\n",
+      "c:\\Users\\user\\anaconda3\\envs\\TacticENV\\lib\\site-packages\\pytorch_lightning\\trainer\\connectors\\data_connector.py:441: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=11` in the `DataLoader` to improve performance.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "97215ced575c46d99b8e3cd20ab809e9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Training: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2d57332f35bf49ab8ff89e3f4215180b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "64ab7eb3058c4e518478349348fcb950",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7c621ba6368341d68765f570aaa2b8db",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4fc7906c46b44fc8814452719f69a6cb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dcdd8be5bf65421db1af4cf9303da835",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cb3f5bc1991b4addb12cd99b9213a57b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "734438becdbc443e87942898e64199a4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0baf18f624a341efba714be21cf42c5f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d39784d032f84f5cbc06b1694beb1acc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b1998c91254e493893112c63a0e0b680",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "`Trainer.fit` stopped: `max_epochs=10` reached.\n"
+     ]
+    }
+   ],
+   "source": [
+    "torch.set_float32_matmul_precision('medium')\n",
+    "trainer = pl.Trainer(\n",
+    "    default_root_dir=\"./runs/example\",\n",
+    "    accelerator=\"auto\",\n",
+    "    max_epochs=10,\n",
+    "    callbacks=[\n",
+    "        ModelCheckpoint(save_weights_only=True, monitor=\"val/loss\", mode=\"min\"),\n",
+    "    ],\n",
+    "    log_every_n_steps=1,\n",
+    ")\n",
+    "\n",
+    "trainer.fit(wrapped_net, train_loader, val_loader)\n",
+    "__best_path = list(Path(\"./runs/example/lightning_logs/version_0/checkpoints/\").glob(\"*\"))[0]\n",
+    "wrapped_net.load_state_dict(torch.load(str(__best_path))['state_dict'])\n",
+    "del __best_path"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/config/openVibe.json b/config/openVibe.json
new file mode 100644
index 0000000..bf02705
--- /dev/null
+++ b/config/openVibe.json
@@ -0,0 +1,25 @@
+{
+    "map_actions": {
+        "769": "OVTK_GDF_Left",
+        "768": "OVTK_GDF_Start_Of_Trial",
+        "33281": "OVTK_StimulationId_Train",
+        "786": "OVTK_GDF_Cross_On_Screen",
+        "32769": "OVTK_StimulationId_ExperimentStart",
+        "32775": "OVTK_StimulationId_BaselineStart",
+        "33553": "OVTK_StimulationId_AddedSamplesBegin",
+        "32776": "OVTK_StimulationId_BaselineStop",
+        "1010": "OVTK_GDF_End_Of_Session",
+        "770": "OVTK_GDF_Right",
+        "781": "OVTK_GDF_Feedback_Continuous",
+        "33282": "OVTK_StimulationId_Beep",
+        "800": "OVTK_GDF_End_Of_Trial",
+        "33552": "OVTK_StimulationId_RemovedSamples",
+        "32770": "OVTK_StimulationId_ExperimentStop",
+        "33554": "OVTK_StimulationId_AddedSamplesEnd"
+    }, 
+    "main_actions": {
+        "left": "769",
+        "right": "770",
+        "stop": "800"
+    }
+}
\ No newline at end of file
diff --git a/core/__init__.py b/core/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/core/io.py b/core/io.py
new file mode 100644
index 0000000..5a8395a
--- /dev/null
+++ b/core/io.py
@@ -0,0 +1,135 @@
+import numpy as np
+import pandas as pd
+
+
+def filter_events(df: pd.DataFrame, config: dict[str, any]) -> pd.DataFrame:
+    """Получение табличной нотации для экспериментов из файлов OpenVibe (.csv)
+
+    Args:
+        df (pd.DataFrame): сырой файл .ov сконвертированный в .csv и загруженный в pandas
+        config (dict[str, any]): словарь с расшифровкой кодов событий OpenVibe
+
+    Returns:
+        pd.DataFrame: фрейм (таблица) в нашей собственной нотации
+    """
+
+    events = []
+    __temp_event = {}
+
+    new_frame = df.copy()
+
+    __ov_events = df[~df["Event Id"].isna()][["Event Id"]]
+    for idx in range(__ov_events.shape[0]):
+        __row_events = __ov_events.iloc[idx, 0].split(":")
+
+        if config["main_actions"]["left"] in __row_events or config["main_actions"]["right"] in __row_events:
+            __temp_event = {"start": __ov_events.index[idx]}
+            if config["main_actions"]["left"] in __row_events:
+                __temp_event["action"] = "left"
+            if config["main_actions"]["right"] in __row_events:
+                __temp_event["action"] = "right"
+
+        # В записи stop повторяется дважды (как и любые другие действия)
+        if config["main_actions"]["stop"] in __row_events and len(__temp_event) > 0:
+            __temp_event["stop"] = __ov_events.index[idx + 1]
+            events.append(__temp_event.copy())
+            __temp_event = {}
+
+    new_frame = new_frame.drop(columns=["Epoch", "Event Id", "Event Date", "Event Duration"])
+    new_frame["action"] = None
+
+    for event in events:
+        new_frame.loc[event["start"] : event["stop"], "action"] = event["action"]
+
+    new_frame["action"] = new_frame["action"].fillna("relax")
+    return new_frame
+
+
+def get_ranges(labels: np.ndarray) -> list[list[int, int, int]]:
+    """Преобразование сырых лейблов временного ряда в отрезки для визуализации
+
+    Args:
+        labels (np.ndarray): лейблы
+
+    Returns:
+        list[list[int, int, int]]: список отрезков для визуализации, каждый отрезок определен как
+            тройка "начало", "конец", "тип действия"
+    """
+
+    actions = []
+    __temp_action = [0]
+    __prev_action = labels[0]
+    idx = 1
+
+    while True:
+        if idx + 1 > len(labels):
+            __temp_action.extend([idx, __prev_action])
+            actions.append(__temp_action)
+            break
+
+        if labels[idx] != __prev_action:
+            __temp_action.extend([idx - 1, __prev_action])
+            actions.append(__temp_action)
+            __temp_action = [idx]
+            __prev_action = labels[idx]
+
+        idx += 1
+
+    return actions
+
+
+def split_epochs_binary(
+    df: pd.DataFrame,
+    epoch_duration: int,
+    init_pause: int,
+    final_pause: int,
+    step: int,
+    map_labels: dict[str, int],
+    cols: list[str],
+) -> tuple[np.ndarray, np.ndarray]:
+    """Формирование бинарного датасета (из отрезков, когда есть действие!)
+
+    Notes:
+        Если нужно использовать только один отрезок на действие - установть step > [отсчеты в отрезке]
+
+        Например, если отрезок действия 4 с (1000 отсчётов для частоты дискретизации 250 Гц), то поставив step=1001
+        будет сформирован только 1 обучающий (валидационный) объект из данного действия.
+
+    Args:
+        df (pd.DataFrame): фрейм с данными
+        epoch_duration (int): длительность эпохи (в отсчетах сигнала, t*f)
+        init_pause (int): начальная пауза - сколько отсчетов выбросить с момента предъявления стимула
+            (в отсчетах сигнала, t*f)
+        final_pause (int): конечная пауза - сколько отсчетов выкинуть в конце (в отсчетах сигнала, t*f)
+        step (int): шаг смещения внутри времени прдъявления стимула (в отсчетах сигнала, t*f)
+        map_labels (dict[str, int]): словарь соотносящий действие и его числовое значение,
+            например, {'left': 0, 'right': 1}
+        cols (list[str]): имена колонок, которые будут добавлены в датасет,
+            например: ['Channel 1', 'Channel 2', 'Channel 3']
+
+    Returns:
+        tuple[np.ndarray, np.ndarray]: кортеж с 2 массивами:
+
+            * обучающие данные, размерность [количество образцов X длительность эпохи X количество каналов]
+
+            * метки классов (в соответсвии с map_labels), размерность [количество образцов X 1]
+
+    Raise:
+        ValueError: словарь map_labels содержит информацию не о 2х классах
+    """
+
+    if len(map_labels) != 2:
+        raise ValueError(f"Словарь map_labels должен содержать 2 класса, получено: {len(map_labels)}!")
+
+    actions = get_ranges(df["action"])
+    x = []
+    y = []
+
+    for action in actions:
+        if action[2] in map_labels.keys():
+            __low_bound = action[0] + init_pause
+            __upper_bound = action[1] - final_pause - epoch_duration + 1
+            for inner_idx in range(__low_bound, __upper_bound, step):
+                x.append(df.loc[inner_idx : inner_idx + epoch_duration - 1, cols].values)
+                y.append(map_labels[action[2]])
+    return np.array(x), np.array(y)
diff --git a/core/nn/__init__.py b/core/nn/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/core/nn/data.py b/core/nn/data.py
new file mode 100644
index 0000000..eeb2b8d
--- /dev/null
+++ b/core/nn/data.py
@@ -0,0 +1,23 @@
+import numpy as np
+import torch
+
+
+class TableDataset(torch.utils.data.Dataset):
+
+    def __init__(self, x: np.ndarray, y: np.ndarray) -> None:
+        """Простой датасет из объектов numpy
+
+        Args:
+            x (np.ndarray): обучающие объекты
+            y (np.ndarray): метки
+        """
+        super().__init__()
+        self.map_labels = None
+        self.x = x
+        self.y = y
+
+    def __len__(self):
+        return len(self.x)
+
+    def __getitem__(self, idx) -> tuple[torch.Tensor, torch.Tensor]:
+        return torch.FloatTensor(self.x[idx]), torch.FloatTensor(self.y[idx])
diff --git a/core/nn/provider.py b/core/nn/provider.py
new file mode 100644
index 0000000..73b6a86
--- /dev/null
+++ b/core/nn/provider.py
@@ -0,0 +1,53 @@
+import pytorch_lightning as pl
+import torch
+
+
+class NetProvider(pl.LightningModule):
+    def __init__(self, net: torch.nn.Module, lr: float, criteria: torch.nn.Module, metrics: list[callable]) -> None:
+        """Базовая обертка для сетей
+
+        Args:
+            net (torch.nn.Module): сеть - модуль pyTorch
+            lr (float): темп обучения
+            criteria (torch.nn.Module): функция ошибки
+            metrics (list[callable]): список метрик - функций, принимающих (preds: np.ndarray, true_labels: np.ndarray)
+        """
+        super().__init__()
+        self.lr = lr
+        self.criteria = criteria
+        self.net = net
+        self.metrics = metrics
+
+    def forward(self, x):
+        return self.net(x)
+
+    def configure_optimizers(self):
+        return torch.optim.AdamW(self.parameters(), lr=self.lr)
+
+    def training_step(self, batch, batch_idx):
+        data, labels = batch
+        preds = self.forward(data)
+        loss = self.criteria(preds, labels)
+        self.log("train/loss", loss, on_step=True, on_epoch=True)
+        for metric in self.metrics:
+            self.log(
+                f"train/{metric.__name__}",
+                metric(preds.cpu().detach().numpy(), labels.cpu().detach().numpy()),
+                on_step=True,
+                on_epoch=True,
+            )
+        return loss
+
+    def validation_step(self, batch, batch_idx):
+        data, labels = batch
+        preds = self.forward(data)
+        loss = self.criteria(preds, labels)
+        self.log("val/loss", loss, on_step=True, on_epoch=True)
+        for metric in self.metrics:
+            self.log(
+                f"val/{metric.__name__}",
+                metric(preds.cpu().detach().numpy(), labels.cpu().detach().numpy()),
+                on_step=True,
+                on_epoch=True,
+            )
+        return loss
diff --git a/core/signal_processing.py b/core/signal_processing.py
new file mode 100644
index 0000000..e3bed40
--- /dev/null
+++ b/core/signal_processing.py
@@ -0,0 +1,134 @@
+import numpy as np
+import pandas as pd
+import scipy
+from scipy.signal import butter, lfilter
+
+
+def __create_constants(low: float, high: float, sample_rate: float, order: int) -> tuple[float, float]:
+    """Вычисление констан полосового фильтра (Баттерворта)
+
+    Args:
+        low (float): нижняя чатсота среза
+        high (float): верхняя частота среза
+        sample_rate (float): частота дискретизации
+        order (int): порядок фильтра
+
+    Returns:
+        tuple[float, float]: константы фильтра, необходимые для вычислений
+    """
+    return butter(order, [low, high], fs=sample_rate, btype="band")
+
+
+def filter_signal(df: pd.DataFrame, cols: list[str], constants: tuple[float, float]) -> pd.DataFrame:
+    """Фильтрация датасета (сигналов с датчиков)
+
+    Args:
+        df (pd.DataFrame): датафрейм с данными из OpenVibe
+        cols (list[str]): список колонок, данные в которых нужно отфильтровать
+        constants (tuple[float, float]): константы для вычисления фильтра (см. __create_constants)
+
+    Returns:
+        pd.DataFrame: входной фрейм с данными, с примененным фильтром
+    """
+    new_df = df.copy()
+    for col in cols:
+        new_df[col] = lfilter(*constants, df[col].values)
+
+    return new_df
+
+
+class CSPFilter:
+    """
+    Реализация CSP фильтра в стиле sklearn.
+    Основано на реализации github.co/Hiroaki-K4/total_perspective_vortex/main/srcs/csp.py
+    """
+
+    def __init__(self, n_components: int) -> None:
+        self.n_components = n_components
+        self.classes = None
+        self.filters = None
+        self.patterns = None
+        self.mean = None
+        self.std = None
+
+    def __get_covariate(self, x: np.ndarray, y: np.ndarray, cls: any) -> np.ndarray:
+        """Получение ковариаций
+
+        Args:
+            x (np.ndarray): отсчеты сигнала
+            y (np.ndarray): метки класса
+            cls (any): текущий класс
+
+        Returns:
+            np.ndarray: матрица ковариаций
+        """
+
+        x_class = x[y == cls]
+        _, _, n_channels = x_class.shape
+        # x_class = np.transpose(x_class, [1, 0, 2])
+        x_class = x_class.reshape(n_channels, -1)
+        cov = np.dot(x_class, x_class.T)
+        return cov
+
+    def fit(self, x: np.ndarray, y: np.ndarray) -> None:
+        """Обучение фильтра
+
+        Args:
+            x (np.ndarray): отсчеты сигнала (числа)
+            y (np.ndarray): метки класса (любые данные)
+        """
+        self.classes = np.unique(y)
+        n_classes = len(self.classes)
+        if n_classes != 2:
+            raise ValueError(f"Количество классов для CSP должно быть = 2 (получено {n_classes})")
+
+        cov_neg = self.__get_covariate(x, y, self.classes[0])
+        cov_pos = self.__get_covariate(x, y, self.classes[1])
+
+        eig_vals, eig_vecs = scipy.linalg.eigh(cov_neg, cov_pos)
+        for i in range(len(eig_vecs)):
+            eig_vecs[i] = eig_vecs[i] / np.linalg.norm(eig_vecs[i])
+
+        sorted_vals = np.argsort(eig_vals)
+        idxs = np.empty_like(sorted_vals)
+        idxs[0::2] = sorted_vals[len(sorted_vals) // 2 :][::-1]
+        idxs[1::2] = sorted_vals[: len(sorted_vals) // 2]
+
+        eig_vecs = eig_vecs[:, idxs]
+        self.filters = eig_vecs.T
+        self.patterns = np.linalg.inv(eig_vecs)
+        pick_filters = self.filters[:, self.n_components]
+
+        x = np.asarray([np.dot(pick_filters, epoch.T) for epoch in x])
+        x = (x**2).mean(axis=1)
+        self.mean = x.mean(axis=0)
+        self.std = x.std(axis=0)
+
+    def transform(self, x: np.ndarray) -> np.ndarray:
+        """Применение фильтра
+
+        Args:
+            x (np.ndarray): отсчеты сигнала (числа)
+
+        Returns:
+            np.ndarray: вторичные параметры (фичи)
+        """
+        pick_filters = self.filters[: self.n_components]
+        x = np.asarray([np.dot(pick_filters, epoch.T) for epoch in x])
+        x = (x**2).mean(axis=1)
+        x -= self.mean
+        x /= self.std
+        return x
+
+    def fit_transform(self, x: np.ndarray, y: np.ndarray) -> np.ndarray:
+        """Обучение с последующим применением ко входным данным
+
+        Args:
+            x (np.ndarray): отсчеты сигнала (числа)
+            y (np.ndarray): метки класса (любые данные)
+
+        Returns:
+            np.ndarray: вторичные параметры (фичи)
+        """
+        self.fit(x, y)
+        return self.transform(x)
diff --git a/core/vis.py b/core/vis.py
new file mode 100644
index 0000000..70abbec
--- /dev/null
+++ b/core/vis.py
@@ -0,0 +1,34 @@
+import numpy as np
+
+
+def get_ranges(labels: np.ndarray) -> list[list[int, int, int]]:
+    """Преобразование сырых лейблов временного ряда в отрезки для визуализации
+
+    Args:
+        labels (np.ndarray): лейблы
+
+    Returns:
+        list[list[int, int, int]]: список отрезков для визуализации, каждый отрезок определен как
+            тройка "начало", "конец", "тип действия"
+    """
+
+    actions = []
+    __temp_action = [0]
+    __prev_action = labels[0]
+    idx = 1
+
+    while True:
+        if idx + 1 > len(labels):
+            __temp_action.extend([idx, __prev_action])
+            actions.append(__temp_action)
+            break
+
+        if labels[idx] != __prev_action:
+            __temp_action.extend([idx - 1, __prev_action])
+            actions.append(__temp_action)
+            __temp_action = [idx]
+            __prev_action = labels[idx]
+
+        idx += 1
+
+    return actions
diff --git a/load_openVibe.ipynb b/load_openVibe.ipynb
new file mode 100644
index 0000000..a0d187c
--- /dev/null
+++ b/load_openVibe.ipynb
@@ -0,0 +1,911 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "from matplotlib import pylab as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "import json\n",
+    "\n",
+    "%matplotlib inline\n",
+    "plt.style.use('bmh')\n",
+    "plt.rcParams.update({'font.size': 14})\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Загрузка 🐱‍👤"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Описание формата csv файла:\n",
+    ">http://openvibe.inria.fr/csv-file-format-description/\n",
+    "\n",
+    "Коды событий:\n",
+    ">http://openvibe.inria.fr/stimulation-codes/"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "openvibe_config = json.load(open(\"./config/openVibe.json\", \"r\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "raw_data = pd.read_csv(\"./Записи/OpenVibe signals/motor-imagery-csp-1-acquisition-[2024.04.18-10.37.21].csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Time:250Hz</th>\n",
+       "      <th>Epoch</th>\n",
+       "      <th>Channel 1</th>\n",
+       "      <th>Channel 2</th>\n",
+       "      <th>Channel 3</th>\n",
+       "      <th>Channel 4</th>\n",
+       "      <th>Channel 5</th>\n",
+       "      <th>Channel 6</th>\n",
+       "      <th>Channel 7</th>\n",
+       "      <th>Channel 8</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Channel 10</th>\n",
+       "      <th>Channel 11</th>\n",
+       "      <th>Channel 12</th>\n",
+       "      <th>Channel 13</th>\n",
+       "      <th>Channel 14</th>\n",
+       "      <th>Channel 15</th>\n",
+       "      <th>Channel 16</th>\n",
+       "      <th>Event Id</th>\n",
+       "      <th>Event Date</th>\n",
+       "      <th>Event Duration</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>171386.796875</td>\n",
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+       "      <td>154487.453125</td>\n",
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+       "      <td>181144.156250</td>\n",
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+       "      <td>158936.093750</td>\n",
+       "      <td>149495.546875</td>\n",
+       "      <td>...</td>\n",
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+       "      <td>165428.828125</td>\n",
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+       "      <td>123783.914062</td>\n",
+       "      <td>155126.984375</td>\n",
+       "      <td>146069.140625</td>\n",
+       "      <td>151094.765625</td>\n",
+       "      <td>132538.375000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.016</td>\n",
+       "      <td>0</td>\n",
+       "      <td>171376.703125</td>\n",
+       "      <td>165396.484375</td>\n",
+       "      <td>154484.875000</td>\n",
+       "      <td>162261.906250</td>\n",
+       "      <td>181142.125000</td>\n",
+       "      <td>166401.875000</td>\n",
+       "      <td>158935.968750</td>\n",
+       "      <td>149490.734375</td>\n",
+       "      <td>...</td>\n",
+       "      <td>179596.703125</td>\n",
+       "      <td>142719.171875</td>\n",
+       "      <td>123783.015625</td>\n",
+       "      <td>155120.093750</td>\n",
+       "      <td>146067.218750</td>\n",
+       "      <td>151092.062500</td>\n",
+       "      <td>132535.734375</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0.020</td>\n",
+       "      <td>0</td>\n",
+       "      <td>171385.546875</td>\n",
+       "      <td>165420.531250</td>\n",
+       "      <td>154489.937500</td>\n",
+       "      <td>162277.234375</td>\n",
+       "      <td>181143.109375</td>\n",
+       "      <td>166408.687500</td>\n",
+       "      <td>158938.078125</td>\n",
+       "      <td>149492.656250</td>\n",
+       "      <td>...</td>\n",
+       "      <td>179601.718750</td>\n",
+       "      <td>142751.265625</td>\n",
+       "      <td>123791.242188</td>\n",
+       "      <td>155167.218750</td>\n",
+       "      <td>146075.421875</td>\n",
+       "      <td>151093.078125</td>\n",
+       "      <td>132542.531250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>6 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Time:250Hz  Epoch      Channel 1      Channel 2      Channel 3  \\\n",
+       "0       0.000      0  171386.796875  165426.734375  154487.453125   \n",
+       "1       0.004      0  171392.593750  165444.875000  154490.781250   \n",
+       "2       0.008      0  171387.046875  165428.828125  154489.281250   \n",
+       "3       0.012      0  171377.921875  165400.750000  154485.890625   \n",
+       "4       0.016      0  171376.703125  165396.484375  154484.875000   \n",
+       "5       0.020      0  171385.546875  165420.531250  154489.937500   \n",
+       "\n",
+       "       Channel 4      Channel 5      Channel 6      Channel 7      Channel 8  \\\n",
+       "0  162283.515625  181144.156250  166410.906250  158936.093750  149495.546875   \n",
+       "1  162296.968750  181143.578125  166415.734375  158936.906250  149496.156250   \n",
+       "2  162286.640625  181143.687500  166412.078125  158937.578125  149494.859375   \n",
+       "3  162266.593750  181143.359375  166404.328125  158937.078125  149492.484375   \n",
+       "4  162261.906250  181142.125000  166401.875000  158935.968750  149490.734375   \n",
+       "5  162277.234375  181143.109375  166408.687500  158938.078125  149492.656250   \n",
+       "\n",
+       "   ...     Channel 10     Channel 11     Channel 12     Channel 13  \\\n",
+       "0  ...  179601.828125  142757.171875  123782.796875  155169.875000   \n",
+       "1  ...  179604.187500  142784.218750  123787.976562  155203.531250   \n",
+       "2  ...  179602.234375  142767.937500  123788.117188  155178.015625   \n",
+       "3  ...  179598.078125  142729.265625  123783.914062  155126.984375   \n",
+       "4  ...  179596.703125  142719.171875  123783.015625  155120.093750   \n",
+       "5  ...  179601.718750  142751.265625  123791.242188  155167.218750   \n",
+       "\n",
+       "      Channel 14     Channel 15     Channel 16  Event Id Event Date  \\\n",
+       "0  146074.296875  151097.203125  132544.546875       NaN        NaN   \n",
+       "1  146079.062500  151096.406250  132547.890625       NaN        NaN   \n",
+       "2  146076.218750  151096.375000  132545.265625       NaN        NaN   \n",
+       "3  146069.140625  151094.765625  132538.375000       NaN        NaN   \n",
+       "4  146067.218750  151092.062500  132535.734375       NaN        NaN   \n",
+       "5  146075.421875  151093.078125  132542.531250       NaN        NaN   \n",
+       "\n",
+       "  Event Duration  \n",
+       "0            NaN  \n",
+       "1            NaN  \n",
+       "2            NaN  \n",
+       "3            NaN  \n",
+       "4            NaN  \n",
+       "5            NaN  \n",
+       "\n",
+       "[6 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data.head(6)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Фильтруем события, для нас интересны стрелки (лево/право)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "----------------------"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def filter_events(df: pd.DataFrame, config: dict[str, any]) -> pd.DataFrame:\n",
+    "    \"\"\"Получение табличной нотации для экспериментов из файлов OpenVibe (.csv)\n",
+    "\n",
+    "    Args:\n",
+    "        df (pd.DataFrame): сырой файл .ov сконвертированный в .csv и загруженный в pandas\n",
+    "        config (dict[str, any]): словарь с расшифровкой кодов событий OpenVibe\n",
+    "\n",
+    "    Returns:\n",
+    "        pd.DataFrame: фрейм (таблица) в нашей собственной нотации\n",
+    "    \"\"\"\n",
+    "\n",
+    "    events = []\n",
+    "    __temp_event = {}\n",
+    "\n",
+    "    new_frame = df.copy()\n",
+    "\n",
+    "    __ov_events = df[~df['Event Id'].isna()][['Event Id']]\n",
+    "    for idx in range(__ov_events.shape[0]):\n",
+    "        __row_events = __ov_events.iloc[idx, 0].split(\":\")\n",
+    "        \n",
+    "        if config['main_actions']['left'] in __row_events or config['main_actions']['right'] in __row_events:\n",
+    "            __temp_event = {\"start\": __ov_events.index[idx]}\n",
+    "            if config['main_actions']['left'] in __row_events:\n",
+    "                __temp_event['action'] = 'left'\n",
+    "            if config['main_actions']['right'] in __row_events:\n",
+    "                __temp_event['action'] = 'right'\n",
+    "\n",
+    "        # В записи stop повторяется дважды (как и любые другие действия)\n",
+    "        if config['main_actions']['stop'] in __row_events and len(__temp_event) > 0:\n",
+    "            __temp_event['stop'] = __ov_events.index[idx + 1]\n",
+    "            events.append(__temp_event.copy())\n",
+    "            __temp_event = {}\n",
+    "\n",
+    "    new_frame = new_frame.drop(columns=['Epoch', 'Event Id', 'Event Date', 'Event Duration'])\n",
+    "    new_frame['action'] = None\n",
+    "\n",
+    "    for event in events:\n",
+    "        new_frame.loc[event['start']: event['stop'], 'action'] = event['action']\n",
+    "\n",
+    "    new_frame['action'] = new_frame['action'].fillna('relax')\n",
+    "    return new_frame"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_ranges(labels: np.ndarray) -> list[list[int, int, int]]:\n",
+    "    \"\"\"Преобразование сырых лейблов временного ряда в отрезки для визуализации\n",
+    "\n",
+    "    Args:\n",
+    "        labels (np.ndarray): лейблы\n",
+    "\n",
+    "    Returns:\n",
+    "        list[list[int, int, int]]: список отрезков для визуализации, каждый отрезок определен как\n",
+    "            тройка \"начало\", \"конец\", \"тип действия\"\n",
+    "    \"\"\"\n",
+    "\n",
+    "    actions = []\n",
+    "    __temp_action = [0]\n",
+    "    __prev_action = labels[0]\n",
+    "    idx = 1\n",
+    "\n",
+    "    while True:\n",
+    "        if idx + 1 > len(labels):\n",
+    "            __temp_action.extend([idx, __prev_action])\n",
+    "            actions.append(__temp_action)\n",
+    "            break\n",
+    "\n",
+    "        if labels[idx] != __prev_action:\n",
+    "            __temp_action.extend([idx - 1, __prev_action])\n",
+    "            actions.append(__temp_action)\n",
+    "            __temp_action = [idx]\n",
+    "            __prev_action = labels[idx]\n",
+    "\n",
+    "        idx += 1\n",
+    "\n",
+    "    return actions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = filter_events(raw_data, openvibe_config)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_data = data.copy()\n",
+    "actions = get_ranges(plot_data['action'])\n",
+    "action_colors = {\"relax\": \"#C0E986\", \"left\": \"#CA86E9\", \"right\": \"#E98B86\"}\n",
+    "action_names = {\"relax\": \"отдых\", \"left\": \"левая рука\", \"right\": \"правая рука\"}\n",
+    "index = 2\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(15, 5))\n",
+    "__main = ax.plot(plot_data.iloc[:, index], color=\"black\", linewidth=.4, label=\"Данные\")\n",
+    "ax.set_xticks(ax.get_xticks())\n",
+    "ax.set_xticklabels([x * 0.004 for x in ax.get_xticks()])\n",
+    "ax.set_xlim(0, 107_776)\n",
+    "\n",
+    "__patches = []\n",
+    "for act in actions:\n",
+    "    __patches.append(plt.axvspan(act[0], act[1], color=action_colors[act[2]], label=action_names[act[2]]))\n",
+    "\n",
+    "ax.set_title(f\"Визуализация активности в сессии 1\\nс датчика \\\"{data.columns[index]}\\\"\")\n",
+    "ax.set_xlabel(\"Время, с\")\n",
+    "ax.set_ylabel(\"Значение сигнала\\n(формат OpenVibe)\")\n",
+    "ax.legend(loc=\"best\", handles=[__main[0]] + __patches[7:10])\n",
+    "plt.show()\n",
+    "\n",
+    "del __main, __patches"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+tfr++++vtV54eLg8PT0b3I+fn5+ef/55SdK6det05MiRWsvz8vI0e/ZsSVKvXr00e/Zs89fJEzk5OSk8PNzeU2qRuXPn1vl1XqpusTFy5EhJ0g8//KDNmzfXWj5jxgwVFxcrODhY8+bNk5ubW737f+CBB8zWee+9916jsZSUlEiSXFxa3sC6oqJCr7zyiiTp/vvvb3CMHQ8PD/N1SE9P16pVqxrdpyS5urq2OJ5//OMfstls6tWrl/7+97/Lyan+t9AXXnhBXbp0kc1m0/vvv9/i47SFZ599VseOHVNwcLAmT57c4Ho5OTmaO3euJOm5555Tv3796l0vMDDQfC3WrVtntvJoS66urrrkkkskyRzPpyEhISGaOXNmvcuef/55devWTVL1OEMnjrH0t7/9TZJ01VVXafz48fXuw9nZWW+++aasVqsKCgr08ccfNxjLTTfdpODgYP3xxx/6/vvv6yyvaal6Yv1zIkfcf44yY8YM7dy5U1arVS+99FKb7rs1dcaJWnO/Z2Vlmf+/9NJLm9Xyq6188cUXuvvuu2Wz2TRgwAB98sknDV4PUvXYa42dY0REhCZMmCCpuhWOYRgNrnvXXXfpxRdflCS99tprZouy+thsNo0YMULr16+X1WrVZ599pp49ezZxdtVqXmfJvtd69uzZstlskqQ5c+YoKCiozjoXXHCBnnrqKUnVr2dj93FLlJeXm/VmfHy8/vGPf9S73t/+9jf16NFDkjRv3rxa52yPmu0b+pzRlJNRxzz66KOqqKhQUlJSo3XeihUrtHHjRknVdWNjYyR6e3srICCgRXG0hcGDB5v/b+o96UQtqSOPr2suu+yylgUJoMMg8Qagw5s2bVqtgWc9PT3Vr18/ff7555Kqk26LFy9WcHBwo/spKipSenq6/vjjD23dulVbt26t9WXmxMHWV61apaKiIknS+PHj7fpy19Z69erVaJfVe+65x/z/t99+W2tZTZLyqquuajQhKUkDBw6UJG3YsKHR9WoG125qf/XZuHGj2YXrlltuaXTdpKQkderUqcmYysvLJUlWq7VFsVRUVOirr76SVJ1oaSjpJlV/yb/44oubjMVRtm7dqjlz5kiqTgL6+fk1uO6yZcvML3NNlXHNay455rzKysrMpGmvXr0aXffWW2+ttwupVP0l/u6775ZUnVg8PsGcmZlpdsVq6nwDAgLMOBo7Xzc3N/O+evvtt2stS09P17Jly+Tu7t7k4PuOuP8cITs72+w2/thjj5ldkttKa+qME9l7v0uq9UX/xB8pHGnVqlW65ZZbVFFRofPOO09ffPFFi8siPz9faWlptd7LavZRs6wxEydONJPSjz32WIPdDR966CEtWbJETk5O+uc//1mrjmjK8RMv2PNa17x/xcXF1UqQnOj4iU9OfM+z1y+//KJjx45JkkaOHNnge7+Li4tGjx4tqbrcf/rpp1Ydt7X3hqPrmG+++cZ8n/x//+//NZpQ/eKLL8z/P/roo80+xsn0zTffmP9v6j3peC2tI9urrgFwcjHGG4DTVmRkpK6//no98cQTioqKqnednJwcvfrqq/rkk0+0c+fORlsC5OTk1Pq7Zlw3SRowYEDbBN1Kffv2bXT5+eefLycnJ9lstlrj3+Xl5WnXrl2SqseAaW5LrYMHDza4rLS01BzPysPDo1n7O97xY9U09sWqJTHl5uZKUq0xmppj27ZtKi4ullSd6J02bVqrYzlw4IC2bt3a6HJ7PProo6qqqlLv3r01ZsyYRmdZPb6MW5JAaey8mlJRUVHrvEtLS5WamqqZM2dqy5YtcnNza3JGzqau8+OX//e//zX//vnnn83nR48ebX4pbkpT53v//ffrpZde0uLFizVjxgyz9c0777wjm82m2267rdEWG464/w4dOtTo9dVU8qUhkyZNUn5+vsLCwvTMM8/UqRdbq2ZsKHvqjBPZe79LUkJCgqKiorR3717961//UmhoqO6///46Y7y1pZ9++knXXnutSktLJUl33nlnva2X65Oenq5XX31VS5cubXQmT6n6vSwuLq7Rde644w698cYbMgxDd999t0JDQ2stf/HFF/Xmm29KkoKCgnT55Zc3K84ax48B1tLXuqyszGx1e/zM5fXp3LmzYmJilJ6e3uDYYC11/H3V1PGPX/777783a8bRhrTm3nBEHXO8yspKM4F29dVX6/LLL2909s2az08hISHq3r17s47hKMe/FxuGoUOHDunbb781W1UPGjSoRS3RWlpH1rSoNQxDjz/+uCoqKnTdddfVGuMNQMdH4g1Ah3fiQLru7u7q1KlTk10Tfv31Vw0bNqxOF9KGnNhN5PgvDmFhYS2I2HEaGjy4hru7u3x9fZWbm1vrvA8dOmTX8RrrOnP8gOMnfmlrDntjqkmQ1admoPSmWj+ejFgmT57caDdQe3z66afmRBSvv/56oy3zJMecV1MyMzPNQbBPNHToUL388svq06dPo/to6jo//npri+u8qfONjY3VsGHD9M0332j+/PmaMGGCKioqzG5a48aNa3R7R9x/b731VrMmZGmJTZs2mZNSvPjii/L29m7zxFtNvWFPnXEie+93qXp4gPnz5+umm25Sbm6uXn/99Ua7XbbWsWPHdOWVV6qwsND8Ev7ss8/q+uuvbzIp/vXXX+vmm29u9n3ZVJfHiooKs5ugxWJRWVmZrr/+erNr53fffaf/+7//M5cfOnRIEydObNH1VvM6+/j4tDiRVNPaTGq6LpCqk2/p6enm9dBax++nqeN37ty53u3s0Zp7wxF1zPFmzZqllJQUubq66rXXXmty/ZrPT6fCZ6eG3ot9fHz00ksv6eGHH252V3N76sju3bvrH//4h5566ikVFhbqwQcfrHeiHgAdG4k3AB1eSEhIi7oBSNVdkG699VYdOXJErq6uGj9+vK677jp1795dAQEBZtekPXv2KD4+XpIabQ13Msf/aUxz4qjvPKqqqsz/P/jgg02OR9Ucx7eoiYmJafH2x8f07bff1voC05jGEq41v9639IvL8bH87W9/0zXXXNOs7Robl6mtlZWV6YknnpBU3R22OS0ras7LxcVFmzZtavZ13Jwvu/ZYuXKlxo0bp3/+85/mOG31aSrOhu7V41/H999/v9mzszbUrfV448aN0zfffKM5c+boiSee0CeffKLs7Gz17t27yVYxjrj/HOGRRx6RzWbT+eefb3bnbUtlZWXmeEf21Bknsvd+rzFkyBD9/vvveuWVV7Ro0aJmzdRor/z8fEnVY4/+61//0jXXXKO8vDyNHj1aq1atavCaP3LkiEaMGKHi4mJ5e3vriSee0LBhwxQfHy8/Pz+zDvruu+80dOhQSY2/l0nV41n+/vvvcnZ21hdffKFHHnlEqamp5vJ9+/ZJqu7yfcEFF2jChAmaM2eO7rzzTnOcxqbUvD+09nW29z2vrdhbF9mjNWXmyDomJyfHbAX+0EMPNVp3n+hU+exUn4KCAj3//POqrKzUU0891axY7a0jJ0yYoAEDBmjGjBlasmRJra7YAE4PJN4AnJG+++477dmzR1L1IM1jxoypd73GfqE+fjDnzMzMRsfSOlmys7MbXV5aWqqCggJJMsdEk2qfS2FhYYsTmfU5vhtjQkJCi7c/PiZXV9dWx5Sfn2/+6l+TTLUnlvLy8jYpn/nz5zc67teCBQua3RVSqm7htnv3blmtVr388svN2qbmvCorK+Xr66vo6OhmH89e0dHRSk9PN/+uqqpSdna2VqxYoWeeeUYbN27UgAEDlJqa2uA91dR1fnzrjoauc8Mw2uR1rHH11VcrMjJSu3fv1rfffmuO99ZUa7cT42qr+2/KlCmNdtldvXp1i7pw//vf/zYnj5gxY4ZDvjD/9ttv5iDm9tQZx2vN/X68iIgITZ8+XZ9//rkOHz6svn376t133zWXDxs2rFbr3tYICgrSt99+q8TERL3++usaPXq01qxZozfeeEMPPfRQvdv85z//MbvULlmyRJdeemm96zW3tVVqaqo5PtVDDz2kK664QsuWLdPFF19caxD4QYMG6f/+7//k4uKif//73/rll19033336bfffmtyTD2bzWaOZWXP63z8jytN1QXHrxMYGNjiY9Xn+P00dfzjl7fm+NnZ2dq/f7+k1r+ftlUdU+PZZ59Vbm6ugoOD9eyzz7Yonra6d1rjxPfi/Px87dq1SzNnztT777+vZ555Rk5OTpo4cWKj+2ltHXnRRRfJ09NTixcvliT95S9/Md8/MjMzNWzYsBbtD8CphckVAJyR/vjjD/P/t912W4PrHZ88OtF5551n/r+xcbROpppZwhry66+/mt2Fju/uFxQUpMjISEnVs43VrNMa69atk1Sd+EhMTGzx9sd3Nzx+kGN7HT9o8TnnnNOibXv27Gl+mWyLWNrawYMHNX36dEnS448/3uzx2tq6jO3h7OysLl266O6779aiRYskVX/JPD65caKmrvPjx3I7/jp35Pk6OzubA7k//fTTWr16tby9vXXnnXc2ua0j7r+2VFJSoieffFKSNGLEiAZnv22tmjpDUrNbTjWkNff7iSZOnKg9e/bIzc1N8+fPV69evcxHW02q4+HhoWXLlpl15ahRo3T11VdLqr6eGppJuOa9LDAwsMGkm9T4e1kNwzB03333qaysTNHR0frrX/8qqTpZ/vXXX5tdQhMTE/Xpp5/KarXK2dlZc+fOlYuLi1JSUsykXWM2b95sTkxkz+tstVrNVlVN1QXZ2dlmor+hLu4tdXzSqqkJE45f3prjt/becFQd8/vvv5szvE6fPr3ZP0DWfH46dOhQrdaUpwJfX1+de+65WrBggYYMGSKpuqV7Y60X26KOrKys1KhRo1RRUaFu3brp5ZdfNuuZ9h4HD0DrkXgDcEY6fmr4mi8AJ7LZbOYHyvoMHjzY7II2a9asFk837whbt27Vr7/+2uDymjGnpLrT1l933XWSpP3792vhwoWtiuPYsWNavny5JOnyyy+3q3XMJZd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",
+      "text/plain": [
+       "<Figure size 1500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1, figsize=(15, 5))\n",
+    "__temp = pd.DataFrame({'Длительность, с': [(x[1] - x[0]) / 250 for x in actions[1:-1]], 'Действие': [x[2] for x in actions[1:-1]]})\n",
+    "sns.boxplot(data=__temp, x='Действие', y='Длительность, с', ax=ax)\n",
+    "ax.set_title('Распределение времени для каждого действия')\n",
+    "del __temp\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> Что делаем с выбросами? Это так и задуманно? (вопрос 1 из сообщения)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Формирование бинарного датасета 👥"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Параметры датасета\n",
+    "\n",
+    "* Полосовой фильтр ✔\n",
+    "    * частота $8-30 Гц$\n",
+    "    * порядок $5$\n",
+    "* Эпохи ✔\n",
+    "    * $0,5 с$ после предъявления стимула\n",
+    "    * $4 c$ - длительность эпохи\n",
+    "    * Насколько смещаемся?\n",
+    "* CSP-фильтр по эпохам ✔\n",
+    "    * количество признаков $1024$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Полосовой фильтр"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.signal import butter, lfilter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def __create_constants(low: float, high: float, sample_rate: float, order: int) -> tuple[float, float]:\n",
+    "    \"\"\"Вычисление констан полосового фильтра (Баттерворта)\n",
+    "\n",
+    "    Args:\n",
+    "        low (float): нижняя чатсота среза\n",
+    "        high (float): верхняя частота среза\n",
+    "        sample_rate (float): частота дискретизации\n",
+    "        order (int): порядок фильтра\n",
+    "\n",
+    "    Returns:\n",
+    "        tuple[float, float]: константы фильтра, необходимые для вычислений\n",
+    "    \"\"\"\n",
+    "    return butter(order, [low, high], fs=sample_rate, btype='band')\n",
+    "\n",
+    "def filter_signal(df: pd.DataFrame, cols: list[str], constants: tuple[float, float]) -> pd.DataFrame:\n",
+    "    \"\"\"Фильтрация датасета (сигналов с датчиков)\n",
+    "\n",
+    "    Args:\n",
+    "        df (pd.DataFrame): датафрейм с данными из OpenVibe\n",
+    "        cols (list[str]): список колонок, данные в которых нужно отфильтровать\n",
+    "        constants (tuple[float, float]): константы для вычисления фильтра (см. __create_constants)\n",
+    "\n",
+    "    Returns:\n",
+    "        pd.DataFrame: входной фрейм с данными, с примененным фильтром\n",
+    "    \"\"\"\n",
+    "    new_df = df.copy()\n",
+    "    for col in cols:\n",
+    "        new_df[col] = lfilter(*constants, df[col].values)\n",
+    "\n",
+    "    return new_df\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "index = 1\n",
+    "\n",
+    "constants = __create_constants(8.0, 30.0, 250.0, 5) \n",
+    "plot_data = filter_signal(plot_data, [data.columns[index]], constants)\n",
+    "actions = get_ranges(plot_data['action'])\n",
+    "action_colors = {\"relax\": \"#C0E986\", \"left\": \"#CA86E9\", \"right\": \"#E98B86\"}\n",
+    "action_names = {\"relax\": \"отдых\", \"left\": \"левая рука\", \"right\": \"правая рука\"}\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(15, 5))\n",
+    "__filtered = ax.plot(plot_data.iloc[1000:, index], color=\"black\", linewidth=.4, label=\"Фильтрованные данные\")\n",
+    "ax.set_xticks(ax.get_xticks())\n",
+    "ax.set_xticklabels([x * 0.004 for x in ax.get_xticks()])\n",
+    "ax.set_xlim(0, 107_776)\n",
+    "\n",
+    "__patches = []\n",
+    "for act in actions:\n",
+    "    __patches.append(plt.axvspan(act[0], act[1], color=action_colors[act[2]], label=action_names[act[2]]))\n",
+    "\n",
+    "ax.set_title(f\"Визуализация активности в сессии 1\\nс датчика \\\"{data.columns[index]}\\\" (пропущена 1000 отсчетов, артефакты фильтрации)\")\n",
+    "ax.set_xlabel(\"Время, с\")\n",
+    "ax.set_ylabel(\"Значение сигнала\\n(формат OpenVibe)\")\n",
+    "ax.legend(loc=\"best\", handles=[__filtered[0]] + __patches[7:10])\n",
+    "plt.show()\n",
+    "\n",
+    "del __patches, __filtered"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "constants = __create_constants(low=8.0, high=30.0, sample_rate=250.0, order=5) \n",
+    "filtered_data = filter_signal(df=data, cols=[x for x in data.columns if \"Channel\" in x], constants=constants)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Скользящее окно\n",
+    "\n",
+    "> Пояснение к вопросу 3 из сообщения\n",
+    "\n",
+    "Разбиение:\n",
+    "\n",
+    "|----------| Стимул ($5 с$)\n",
+    "\n",
+    "|-xxxxxxxx-| Эпоха 1 ($0,5 с$ паузы + $4 с$ данных)\n",
+    "\n",
+    "|--xxxxxxxx| Эпоха 2 ($0,5 с$ паузы + $0,5 с$ смещение окна + $4 с$ данных)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def split_epochs_binary(\n",
+    "    df: pd.DataFrame, \n",
+    "    epoch_duration: int, \n",
+    "    init_pause: int, \n",
+    "    final_pause: int,\n",
+    "    step: int,\n",
+    "    map_labels: dict[str, int],\n",
+    "    cols: list[str]\n",
+    ") -> tuple[np.ndarray, np.ndarray]:\n",
+    "    \"\"\"Формирование бинарного датасета (из отрезков, когда есть действие!)\n",
+    "\n",
+    "    Notes:\n",
+    "        Если нужно использовать только один отрезок на действие - установть step > [отсчеты в отрезке]\n",
+    "\n",
+    "        Например, если отрезок действия 4 с (1000 отсчётов для частоты дискретизации 250 Гц), то поставив step=1001\n",
+    "        будет сформирован только 1 обучающий (валидационный) объект из данного действия.\n",
+    "\n",
+    "    Args:\n",
+    "        df (pd.DataFrame): фрейм с данными \n",
+    "        epoch_duration (int): длительность эпохи (в отсчетах сигнала, t*f)\n",
+    "        init_pause (int): начальная пауза - сколько отсчетов выбросить с момента предъявления стимула (в отсчетах сигнала, t*f)\n",
+    "        final_pause (int): конечная пауза - сколько отсчетов выкинуть в конце (в отсчетах сигнала, t*f)\n",
+    "        step (int): шаг смещения внутри времени прдъявления стимула (в отсчетах сигнала, t*f)\n",
+    "        map_labels (dict[str, int]): словарь соотносящий действие и его числовое значение, например, {'left': 0, 'right': 1}\n",
+    "        cols (list[str]): имена колонок, которые будут добавлены в датасет, например: ['Channel 1', 'Channel 2', 'Channel 3']\n",
+    "\n",
+    "    Returns:\n",
+    "        tuple[np.ndarray, np.ndarray]: кортеж с 2 массивами: \n",
+    "            \n",
+    "            * обучающие данные, размерность [количество образцов X длительность эпохи X количество каналов]\n",
+    "\n",
+    "            * метки классов (в соответсвии с map_labels), размерность [количество образцов X 1]\n",
+    "\n",
+    "    Raise:\n",
+    "        ValueError: словарь map_labels содержит информацию не о 2х классах\n",
+    "    \"\"\"\n",
+    "\n",
+    "    if len(map_labels) != 2:\n",
+    "        raise ValueError(f\"Словарь map_labels должен содержать 2 класса, получено: {len(map_labels)}!\")\n",
+    "    \n",
+    "    actions = get_ranges(df['action'])\n",
+    "    x = []\n",
+    "    y = []\n",
+    "    \n",
+    "    for action in actions:\n",
+    "        if action[2] in map_labels.keys():\n",
+    "            __low_bound = action[0] + init_pause\n",
+    "            __upper_bound = action[1] - final_pause - epoch_duration + 1\n",
+    "            for inner_idx in range(__low_bound, __upper_bound, step):\n",
+    "                x.append(df.loc[inner_idx: inner_idx + epoch_duration - 1, cols].values)\n",
+    "                y.append(map_labels[action[2]])\n",
+    "    return np.array(x), np.array(y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = split_epochs_binary(\n",
+    "    df=filtered_data, \n",
+    "    epoch_duration=4 * 250, \n",
+    "    init_pause=int(0.5 * 250), \n",
+    "    final_pause=0, \n",
+    "    step=int(5 * 250), \n",
+    "    map_labels={\"left\": 0, \"right\": 1},\n",
+    "    cols=[x for x in data.columns if \"Channel\" in x]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((40, 1000, 16), (40,))"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x.shape, y.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### CSP фильтр"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import scipy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class CSPFilter:\n",
+    "    \"\"\"\n",
+    "    Реализация CSP фильтра в стиле sklearn. \n",
+    "    Основано на реализации github.co/Hiroaki-K4/total_perspective_vortex/main/srcs/csp.py\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    def __init__(self, n_components: int) -> None:\n",
+    "        self.n_components = n_components\n",
+    "        self.classes = None\n",
+    "        self.filters = None\n",
+    "        self.patterns = None\n",
+    "        self.mean = None\n",
+    "        self.std = None\n",
+    "\n",
+    "    def __get_covariate(self, x: np.ndarray, y: np.ndarray, cls: any) -> np.ndarray:\n",
+    "        \"\"\"Получение ковариаций\n",
+    "\n",
+    "        Args:\n",
+    "            x (np.ndarray): отсчеты сигнала\n",
+    "            y (np.ndarray): метки класса\n",
+    "            cls (any): текущий класс\n",
+    "    \n",
+    "        Returns:\n",
+    "            np.ndarray: матрица ковариаций\n",
+    "        \"\"\"\n",
+    "        \n",
+    "        x_class = x[y == cls]\n",
+    "        _, _, n_channels = x_class.shape\n",
+    "        # x_class = np.transpose(x_class, [1, 0, 2])\n",
+    "        x_class = x_class.reshape(n_channels, -1)\n",
+    "        cov = np.dot(x_class, x_class.T)\n",
+    "        return cov\n",
+    "    \n",
+    "    def fit(self, x: np.ndarray, y: np.ndarray) -> None:\n",
+    "        \"\"\"Обучение фильтра\n",
+    "\n",
+    "        Args:\n",
+    "            x (np.ndarray): отсчеты сигнала (числа)\n",
+    "            y (np.ndarray): метки класса (любые данные)\n",
+    "        \"\"\"\n",
+    "        self.classes = np.unique(y)\n",
+    "        n_classes = len(self.classes)\n",
+    "        if n_classes != 2:\n",
+    "            raise ValueError(f\"Количество классов для CSP должно быть = 2 (получено {n_classes})\")\n",
+    "\n",
+    "        cov_neg = self.__get_covariate(x, y, self.classes[0])\n",
+    "        cov_pos = self.__get_covariate(x, y, self.classes[1])\n",
+    "\n",
+    "        eig_vals, eig_vecs = scipy.linalg.eigh(cov_neg, cov_pos)\n",
+    "        for i in range(len(eig_vecs)):\n",
+    "            eig_vecs[i] = eig_vecs[i] / np.linalg.norm(eig_vecs[i])\n",
+    "\n",
+    "        sorted_vals = np.argsort(eig_vals)\n",
+    "        idxs = np.empty_like(sorted_vals)\n",
+    "        idxs[0::2] = sorted_vals[len(sorted_vals) // 2 :][::-1]\n",
+    "        idxs[1::2] = sorted_vals[: len(sorted_vals) // 2]\n",
+    "        \n",
+    "        eig_vecs = eig_vecs[:, idxs]\n",
+    "        self.filters = eig_vecs.T\n",
+    "        self.patterns = np.linalg.inv(eig_vecs)\n",
+    "        pick_filters = self.filters[:, self.n_components]\n",
+    "\n",
+    "        x = np.asarray([np.dot(pick_filters, epoch.T) for epoch in x])\n",
+    "        x = (x ** 2).mean(axis=1)\n",
+    "        self.mean = x.mean(axis=0)\n",
+    "        self.std = x.std(axis=0)\n",
+    "\n",
+    "    def transform(self, x: np.ndarray) -> np.ndarray:\n",
+    "        \"\"\"Применение фильтра\n",
+    "\n",
+    "        Args:\n",
+    "            x (np.ndarray): отсчеты сигнала (числа)\n",
+    "            \n",
+    "        Returns:\n",
+    "            np.ndarray: вторичные параметры (фичи)\n",
+    "        \"\"\"\n",
+    "        pick_filters = self.filters[: self.n_components]\n",
+    "        x = np.asarray([np.dot(pick_filters, epoch.T) for epoch in x])\n",
+    "        x = (x ** 2).mean(axis=1)\n",
+    "        x -= self.mean\n",
+    "        x /= self.std\n",
+    "        return x\n",
+    "\n",
+    "    def fit_transform(self, x: np.ndarray, y: np.ndarray) -> np.ndarray:\n",
+    "        \"\"\"Обучение с последующим применением ко входным данным\n",
+    "\n",
+    "        Args:\n",
+    "            x (np.ndarray): отсчеты сигнала (числа)\n",
+    "            y (np.ndarray): метки класса (любые данные)\n",
+    "            \n",
+    "        Returns:\n",
+    "            np.ndarray: вторичные параметры (фичи)\n",
+    "        \"\"\"\n",
+    "        self.fit(x, y)\n",
+    "        return self.transform(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(71, 1000)"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "csp_filter = CSPFilter(4)\n",
+    "csp_filter.fit(x, y)\n",
+    "csp_filter.transform(x).shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Записи/02 Записи.docx b/Записи/02 Записи.docx
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diff --git a/Записи/_Описание.txt b/Записи/_Описание.txt
new file mode 100644
index 0000000..1aa761e
--- /dev/null
+++ b/Записи/_Описание.txt
@@ -0,0 +1,70 @@
+
+=================================================
+�������� ������� csv �����
+http://openvibe.inria.fr/csv-file-format-description/
+���� �������:
+http://openvibe.inria.fr/stimulation-codes/
+
+
+
+������ ������:
+
+Time:250Hz,
+Epoch,
+Channel 1,
+Channel 2,
+Channel 3,
+Channel 4,
+Channel 5,
+Channel 6,
+Channel 7,
+Channel 8,
+Channel 9,
+Channel 10,
+Channel 11,
+Channel 12,
+Channel 13,
+Channel 14,
+Channel 15,
+Channel 16,
+Event Id,
+Event Date,
+	
+
+
+������� ���������� ������ ����������� � �������� ������ �������:
+
+����
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diff --git a/Записи/Ответы_24.04.24.docx b/Записи/Ответы_24.04.24.docx
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diff --git a/Пояснения/01 Подкл электродов 16 отв.docx b/Пояснения/01 Подкл электродов 16 отв.docx
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diff --git a/Пояснения/03 Расчеты.xlsx b/Пояснения/03 Расчеты.xlsx
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index 0000000..37fe890
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diff --git a/Пояснения/_Описание.txt b/Пояснения/_Описание.txt
new file mode 100644
index 0000000..1aa761e
--- /dev/null
+++ b/Пояснения/_Описание.txt
@@ -0,0 +1,70 @@
+
+=================================================
+�������� ������� csv �����
+http://openvibe.inria.fr/csv-file-format-description/
+���� �������:
+http://openvibe.inria.fr/stimulation-codes/
+
+
+
+������ ������:
+
+Time:250Hz,
+Epoch,
+Channel 1,
+Channel 2,
+Channel 3,
+Channel 4,
+Channel 5,
+Channel 6,
+Channel 7,
+Channel 8,
+Channel 9,
+Channel 10,
+Channel 11,
+Channel 12,
+Channel 13,
+Channel 14,
+Channel 15,
+Channel 16,
+Event Id,
+Event Date,
+	
+
+
+������� ���������� ������ ����������� � �������� ������ �������:
+
+����
+32.9839999469,257,175321.6875000000,161476.5781250000,157384.8906250000,165382.3125000000,182353.1093750000,163875.0781250000,160708.5000000000,162043.2968750000,150507.6562500000,177546.5781250000,156140.7187500000,132061.4531250000,152687.8437500000,153717.4062500000,163318.2500000000,154924.4218750000,
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diff --git a/Пояснения/first_look.pdf b/Пояснения/first_look.pdf
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diff --git a/Пояснения/вопросы_25.04.txt b/Пояснения/вопросы_25.04.txt
new file mode 100644
index 0000000..89d8da4
--- /dev/null
+++ b/Пояснения/вопросы_25.04.txt
@@ -0,0 +1,4 @@
+1. � ������ ����������� ��������� ������� �� ������� ������������ ������� (����/�����) ��. pdf. � ��������� ���� �������� �� ������������ ������������ ��� ��� ������ ������? >90% �������� - 5 �. ������, 1-3 ������� (� �������) ������� - �� 7 �. (��. pdf).
+2. ����� ��������� ����� ��� ������������ � 3��? ������ � ������� 'Channel #'
+3. ���� ����� �� 4 � ����� ������������ ������� (� ������ ����� � 0,5 �), �� ����� ����� ��� ��������� ������ ������� ������������ �������? ����� ������ ������ - �� ����� ������� (�� ������ ��������� ������� � ���� �� ��������� 1/250�). ���� ������ ����� 0,5 � (������� ����������� ���� - ����� ����� ���� �������� ������� ������������ ������� ���������� ����������, ��. pdf).
+4. ���������� CSP ����� ����������.