Convert_signals_to_Time_Domain_Visualization.ipynb

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    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyO4xKwaMrqqOmg8imQ57J3Z",
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      "name": "python3",
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      "name": "python"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Armin-Abdollahi/Signal-Processing/blob/main/Convert_signals_to_Time_Domain_Visualization.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Time-Domain Visualization\n",
        "\n",
        "Time-domain visualization is a way of representing and analyzing signals by plotting the signal's amplitude (y-axis) against time (x-axis).\n",
        "\n",
        "### Characteristics of Time-Domain Visualization\n",
        "\n",
        "- Displays how the signal changes over time\n",
        "- Amplitude is typically plotted on the y-axis and time on the x-axis\n",
        "- Useful for analyzing transient responses, wave propagation, and energy flow in circuits and systems\n",
        "- Commonly used with oscilloscopes to view real-world signals in the time domain\n",
        "\n",
        "### Advantages of Time-Domain Analysis\n",
        "\n",
        "- Provides insight into the flow of mechanical and electrical energies in a system\n",
        "- Enables separation of desired signals from extraneous signals like reflections and noise based on timing\n",
        "- Useful for circuit designs with antennas where stray signals need to be identified"
      ],
      "metadata": {
        "id": "CmR_wdwJDEh_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n17xbyxMMm37",
        "outputId": "9255064d-e872-4095-8b91-9d61f34086a6"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install --upgrade mne"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OAdstaBgMoD1",
        "outputId": "39429a17-7453-4073-f92e-196fa7071bee"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: mne in /usr/local/lib/python3.10/dist-packages (1.8.0)\n",
            "Requirement already satisfied: decorator in /usr/local/lib/python3.10/dist-packages (from mne) (4.4.2)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from mne) (3.1.4)\n",
            "Requirement already satisfied: lazy-loader>=0.3 in /usr/local/lib/python3.10/dist-packages (from mne) (0.4)\n",
            "Requirement already satisfied: matplotlib>=3.6 in /usr/local/lib/python3.10/dist-packages (from mne) (3.7.1)\n",
            "Requirement already satisfied: numpy<3,>=1.23 in /usr/local/lib/python3.10/dist-packages (from mne) (1.26.4)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from mne) (24.1)\n",
            "Requirement already satisfied: pooch>=1.5 in /usr/local/lib/python3.10/dist-packages (from mne) (1.8.2)\n",
            "Requirement already satisfied: scipy>=1.9 in /usr/local/lib/python3.10/dist-packages (from mne) (1.13.1)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from mne) (4.66.5)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.6->mne) (1.2.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.6->mne) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.6->mne) (4.53.1)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.6->mne) (1.4.5)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.6->mne) (9.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.6->mne) (3.1.2)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.6->mne) (2.8.2)\n",
            "Requirement already satisfied: platformdirs>=2.5.0 in /usr/local/lib/python3.10/dist-packages (from pooch>=1.5->mne) (4.2.2)\n",
            "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.10/dist-packages (from pooch>=1.5->mne) (2.32.3)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->mne) (2.1.5)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib>=3.6->mne) (1.16.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->pooch>=1.5->mne) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->pooch>=1.5->mne) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->pooch>=1.5->mne) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->pooch>=1.5->mne) (2024.7.4)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import mne"
      ],
      "metadata": {
        "id": "6-rvJb6MMpD9"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the EEG data\n",
        "raw = mne.io.read_raw_gdf('/content/drive/MyDrive/BCI_Database/gdf/A1_CE_baseline.gdf', preload=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uHyEhY52MqZq",
        "outputId": "898c5364-f88d-448e-927d-3537d7bc4e9f"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting EDF parameters from /content/drive/MyDrive/BCI_Database/gdf/A1_CE_baseline.gdf...\n",
            "GDF file detected\n",
            "Setting channel info structure...\n",
            "Could not determine channel type of the following channels, they will be set as EEG:\n",
            "Fz, FCz, Cz, CPz, Pz, C1, C3, C5, C2, C4, C6, EOG1, EOG2, EOG3, EMGg, EMGd, F4, FC2, FC4, FC6, CP2, CP4, CP6, P4, F3, FC1, FC3, FC5, CP1, CP3, CP5, P3\n",
            "Creating raw.info structure...\n",
            "Reading 0 ... 95775  =      0.000 ...   187.061 secs...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Select only EEG channels\n",
        "raw.pick_types(meg=False, eeg=True, eog=False, exclude='bads')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415
        },
        "id": "n_E3COoQMrqu",
        "outputId": "b419f1bb-586d-41e6-dc86-6cb5d8900077"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "NOTE: pick_types() is a legacy function. New code should use inst.pick(...).\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<RawGDF | A1_CE_baseline.gdf, 32 x 95776 (187.1 s), ~23.4 MB, data loaded>"
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              "        \n",
              "        A1_CE_baseline.gdf\n",
              "        \n",
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              "    \n",
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              "    <td>Time points</td>\n",
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              "    \n",
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              "    <td>EEG</td>\n",
              "    <td>\n",
              "        <button class=\"channel-names-btn\" onclick=\"alert('Good EEG:\\n\\nFz, FCz, Cz, CPz, Pz, C1, C3, C5, C2, C4, C6, EOG1, EOG2, EOG3, EMGg, EMGd, F4, FC2, FC4, FC6, CP2, CP4, CP6, P4, F3, FC1, FC3, FC5, CP1, CP3, CP5, P3')\" title=\"(Click to open in popup)&#13;&#13;Fz, FCz, Cz, CPz, Pz, C1, C3, C5, C2, C4, C6, EOG1, EOG2, EOG3, EMGg, EMGd, F4, FC2, FC4, FC6, CP2, CP4, CP6, P4, F3, FC1, FC3, FC5, CP1, CP3, CP5, P3\">\n",
              "            32\n",
              "        </button>\n",
              "\n",
              "        \n",
              "    </td>\n",
              "</tr>\n",
              "\n",
              "\n",
              "<tr class=\"repr-element channels-317a1687-5929-43b7-846b-07c7d7fd738e \">\n",
              "    <td class=\"repr-section-toggle-col\"></td>\n",
              "    <td>Head & sensor digitization</td>\n",
              "    \n",
              "    <td>Not available</td>\n",
              "    \n",
              "</tr>\n",
              "    \n",
              "\n",
              "\n",
              "\n",
              "\n",
              "\n",
              "\n",
              "\n",
              "<tr class=\"repr-section-header filters-eb693b64-cf07-42bc-9396-258752455c9a\"  title=\"Hide section\" \n",
              "    onclick=\"toggleVisibility('filters-eb693b64-cf07-42bc-9396-258752455c9a')\">\n",
              "    <th class=\"repr-section-toggle-col\">\n",
              "        <button>\n",
              "            \n",
              "            <span class=\"collapse-uncollapse-caret\"></span>\n",
              "        </button>\n",
              "    </th>\n",
              "    <th colspan=\"2\">\n",
              "        <strong>Filters</strong>\n",
              "    </th>\n",
              "</tr>\n",
              "\n",
              "<tr class=\"repr-element filters-eb693b64-cf07-42bc-9396-258752455c9a \">\n",
              "    <td class=\"repr-section-toggle-col\"></td>\n",
              "    <td>Highpass</td>\n",
              "    <td>0.00 Hz</td>\n",
              "</tr>\n",
              "\n",
              "\n",
              "<tr class=\"repr-element filters-eb693b64-cf07-42bc-9396-258752455c9a \">\n",
              "    <td class=\"repr-section-toggle-col\"></td>\n",
              "    <td>Lowpass</td>\n",
              "    <td>256.00 Hz</td>\n",
              "</tr>\n",
              "\n",
              "\n",
              "</table>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Extract data and times\n",
        "data, times = raw[:]"
      ],
      "metadata": {
        "id": "_vOy8mHPMtBm"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Select the data for a single channel, e.g., the first EEG channel\n",
        "channel_data = data[0]"
      ],
      "metadata": {
        "id": "L-gLiooGMuko"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot the time-domain signal\n",
        "plt.figure(figsize=(15, 5))\n",
        "plt.plot(times, channel_data)\n",
        "plt.title('EEG Signal in Time Domain')\n",
        "plt.xlabel('Time [sec]')\n",
        "plt.ylabel('Amplitude [µV]')\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "QjUZVxzwMlJp",
        "outputId": "23690de5-ae45-4d83-fa8e-956705305066"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Using the MNE library to visualize time-domain signals for all .gdf files in a specified folder and then saves the plots to a specified output folder"
      ],
      "metadata": {
        "id": "hgzzYBMiMw9T"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LcQhdq9jDBp-",
        "outputId": "d7c10459-072e-4a49-89b0-afa598cb41e6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install --upgrade mne"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ynwn6F-mDIiD",
        "outputId": "e7e8cc06-c5ab-45ff-d10a-200d3c417b36"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: mne in /usr/local/lib/python3.10/dist-packages (1.8.0)\n",
            "Requirement already satisfied: decorator in /usr/local/lib/python3.10/dist-packages (from mne) (4.4.2)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from mne) (3.1.4)\n",
            "Requirement already satisfied: lazy-loader>=0.3 in /usr/local/lib/python3.10/dist-packages (from mne) (0.4)\n",
            "Requirement already satisfied: matplotlib>=3.6 in /usr/local/lib/python3.10/dist-packages (from mne) (3.7.1)\n",
            "Requirement already satisfied: numpy<3,>=1.23 in /usr/local/lib/python3.10/dist-packages (from mne) (1.26.4)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from mne) (24.1)\n",
            "Requirement already satisfied: pooch>=1.5 in /usr/local/lib/python3.10/dist-packages (from mne) (1.8.2)\n",
            "Requirement already satisfied: scipy>=1.9 in /usr/local/lib/python3.10/dist-packages (from mne) (1.13.1)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from mne) (4.66.5)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.6->mne) (1.2.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.6->mne) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.6->mne) (4.53.1)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.6->mne) (1.4.5)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.6->mne) (9.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.6->mne) (3.1.2)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.6->mne) (2.8.2)\n",
            "Requirement already satisfied: platformdirs>=2.5.0 in /usr/local/lib/python3.10/dist-packages (from pooch>=1.5->mne) (4.2.2)\n",
            "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.10/dist-packages (from pooch>=1.5->mne) (2.32.3)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->mne) (2.1.5)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib>=3.6->mne) (1.16.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->pooch>=1.5->mne) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->pooch>=1.5->mne) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->pooch>=1.5->mne) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->pooch>=1.5->mne) (2024.7.4)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import mne\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "BNCyaQnADKQZ"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the folder containing the .gdf files and the folder to save the plots\n",
        "input_folder_path = '/content/drive/MyDrive/BCI_Database/gdf'\n",
        "output_folder_path = '/content/drive/MyDrive/BCI_Database/gdf/Time_Domain_Visualization_Images/'"
      ],
      "metadata": {
        "id": "gHdtuAdwDMP7"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Ensure the output folder exists\n",
        "os.makedirs(output_folder_path, exist_ok=True)"
      ],
      "metadata": {
        "id": "PAHGnX9gDTVa"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Loop through all files in the input folder\n",
        "for filename in os.listdir(input_folder_path):\n",
        "    if filename.endswith('.gdf'):\n",
        "        # Construct the full file path\n",
        "        file_path = os.path.join(input_folder_path, filename)\n",
        "\n",
        "        # Load the .gdf file\n",
        "        raw = mne.io.read_raw_gdf(file_path, preload=True)\n",
        "\n",
        "        # Extract data from the raw object\n",
        "        data = raw.get_data()\n",
        "        times = raw.times\n",
        "\n",
        "        # Loop through each channel in the data\n",
        "        for i, channel_data in enumerate(data):\n",
        "            # Plot the time-domain signal\n",
        "            plt.figure(figsize=(10, 4))\n",
        "            plt.plot(times, channel_data)\n",
        "            plt.xlabel('Time (s)')\n",
        "            plt.ylabel('Amplitude')\n",
        "            plt.title(f'Time-Domain Signal - {filename} - Channel {i}')\n",
        "\n",
        "            # Save the plot as an image\n",
        "            plot_file_path = os.path.join(output_folder_path, f'time_domain_{filename}_channel_{i}.png')\n",
        "            plt.savefig(plot_file_path)\n",
        "            plt.close()\n",
        "\n",
        "        print(f'Time-domain plots created and saved for {filename} in {output_folder_path}')\n",
        "\n",
        "print('Time-domain visualization complete for all .gdf files in the folder.')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZdzLFZI7IEst",
        "outputId": "422ee044-3d7c-4745-a7d9-721ccc1aea06"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting EDF parameters from /content/drive/MyDrive/BCI_Database/gdf/A1_CE_baseline.gdf...\n",
            "GDF file detected\n",
            "Setting channel info structure...\n",
            "Could not determine channel type of the following channels, they will be set as EEG:\n",
            "Fz, FCz, Cz, CPz, Pz, C1, C3, C5, C2, C4, C6, EOG1, EOG2, EOG3, EMGg, EMGd, F4, FC2, FC4, FC6, CP2, CP4, CP6, P4, F3, FC1, FC3, FC5, CP1, CP3, CP5, P3\n",
            "Creating raw.info structure...\n",
            "Reading 0 ... 95775  =      0.000 ...   187.061 secs...\n",
            "Time-domain plots created and saved for A1_CE_baseline.gdf in /content/drive/MyDrive/BCI_Database/gdf/Time_Domain_Visualization_Images/\n",
            "Extracting EDF parameters from /content/drive/MyDrive/BCI_Database/gdf/C85_R2_acquisition.gdf...\n",
            "GDF file detected\n",
            "Setting channel info structure...\n",
            "Could not determine channel type of the following channels, they will be set as EEG:\n",
            "Fz, FCz, Cz, CPz, Pz, C1, C3, C5, C2, C4, C6, EOG1, EOG2, EOG3, EMGg, EMGd, F4, FC2, FC4, FC6, CP2, CP4, CP6, P4, F3, FC1, FC3, FC5, CP1, CP3, CP5, P3\n",
            "Creating raw.info structure...\n",
            "Reading 0 ... 230431  =      0.000 ...   450.061 secs...\n",
            "Time-domain plots created and saved for C85_R2_acquisition.gdf in /content/drive/MyDrive/BCI_Database/gdf/Time_Domain_Visualization_Images/\n",
            "Extracting EDF parameters from /content/drive/MyDrive/BCI_Database/gdf/A4_R3_onlineT.gdf...\n",
            "GDF file detected\n",
            "Setting channel info structure...\n",
            "Could not determine channel type of the following channels, they will be set as EEG:\n",
            "Fz, FCz, Cz, CPz, Pz, C1, C3, C5, C2, C4, C6, EOG1, EOG2, EOG3, EMGg, EMGd, F4, FC2, FC4, FC6, CP2, CP4, CP6, P4, F3, FC1, FC3, FC5, CP1, CP3, CP5, P3\n",
            "Creating raw.info structure...\n",
            "Reading 0 ... 230431  =      0.000 ...   450.061 secs...\n",
            "Time-domain plots created and saved for A4_R3_onlineT.gdf in /content/drive/MyDrive/BCI_Database/gdf/Time_Domain_Visualization_Images/\n",
            "Extracting EDF parameters from /content/drive/MyDrive/BCI_Database/gdf/B66_R3_onlineT.gdf...\n",
            "GDF file detected\n",
            "Setting channel info structure...\n",
            "Could not determine channel type of the following channels, they will be set as EEG:\n",
            "Fz, FCz, Cz, CPz, Pz, C1, C3, C5, C2, C4, C6, EOG1, EOG2, EOG3, EMGg, EMGd, F4, FC2, FC4, FC6, CP2, CP4, CP6, P4, F3, FC1, FC3, FC5, CP1, CP3, CP5, P3\n",
            "Creating raw.info structure...\n",
            "Reading 0 ... 261599  =      0.000 ...   510.936 secs...\n",
            "Time-domain plots created and saved for B66_R3_onlineT.gdf in /content/drive/MyDrive/BCI_Database/gdf/Time_Domain_Visualization_Images/\n",
            "Time-domain visualization complete for all .gdf files in the folder.\n"
          ]
        }
      ]
    }
  ]
}