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EEG hand movement imagry abcd.ows
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<scheme description="A EEG Dataset of motor movement imagry trying to deep train a neural network to detect movements. https://www.kaggle.com/datasets/aymanmostafa11/eeg-motor-imagery-bciciv-2a https://www.kaggle.com/datasets/fabriciotorquato/eeg-data-from-hands-movement Pre-processing: The data collected in the different situations mentioned above need to be pre-processed for later use in the machine learning component. For to better understand pre-processing, both the data source and the format and characteristics are discussed below. The 128-Hz, 14-channel EEG equipment provides a 16-bit 128x14 matrix at each reading. As the brainwaves of interest are in the range of 0 to 30 Hz (Table 1), this information collected is passed by the Fast Fourier Transform (FFT) algorithm in the frequency range 0 to 30 Hz. , resulting in a 30x14 matrix in the frequency domain. After the transformation of the data collected for the frequency domain, the weighted and arithmetic mean of each wave was performed, so that the resulting matrix has the dimensions 14x4x2. Thus, each instant of data collected is represented by the weighted and arithmetic mean for each of the 14 device channels and the 4 wave classifications." title="EEG Hand movement imagry abcd data" version="2.0">
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<text font-family="MS Shell Dlg 2" font-size="16" id="0" rect="(67.0, 448.0, 640.0, 368.0)" type="text/plain">A EEG Dataset of motor movement imagry trying to deep train a neural network to detect movements.
https://www.kaggle.com/datasets/aymanmostafa11/eeg-motor-imagery-bciciv-2a
https://www.kaggle.com/datasets/fabriciotorquato/eeg-data-from-hands-movement
Pre-processing:
The data collected in the different situations mentioned above need to be pre-processed for later use in the machine learning component. For to better understand pre-processing, both the data source and the format and characteristics are discussed below.
The 128-Hz, 14-channel EEG equipment provides a 16-bit 128x14 matrix at each reading. As the brainwaves of interest are in the range of 0 to 30 Hz (Table 1), this information collected is passed by the Fast Fourier Transform (FFT) algorithm in the frequency range 0 to 30 Hz. , resulting in a 30x14 matrix in the frequency domain.
After the transformation of the data collected for the frequency domain, the weighted and arithmetic mean of each wave was performed, so that the resulting matrix has the dimensions 14x4x2. Thus, each instant of data collected is represented by the weighted and arithmetic mean for each of the 14 device channels and the 4 wave classifications.</text>
<text font-family="MS Shell Dlg 2" font-size="16" id="1" rect="(1214.0, 135.0, 142.0, 60.0)" type="text/plain">73 79 74</text>
<text font-family="MS Shell Dlg 2" font-size="16" id="2" rect="(614.0, 120.0, 150.0, 31.0)" type="text/plain">40000,
400000
</text>
<text font-family="MS Shell Dlg 2" font-size="16" id="3" rect="(278.0, 219.0, 150.0, 27.0)" type="text/plain">m not std
m not std</text>
<text font-family="MS Shell Dlg 2" font-size="16" id="4" rect="(89.0, -128.0, 150.0, 27.0)" type="text/plain">notations m for window transrorm mean
I think after fft to delta and different wave form powers 4 bins
</text>
<text font-family="MS Shell Dlg 2" font-size="16" id="5" rect="(587.0, -129.0, 150.0, 27.0)" type="text/plain">Number of neurons layers in last completed model </text>
<text font-family="MS Shell Dlg 2" font-size="16" id="6" rect="(1219.0, 31.0, 150.0, 27.0)" type="text/plain">correct diagonal to compare neuron layers to acuracy </text>
<text font-family="MS Shell Dlg 2" font-size="16" id="7" rect="(1221.0, 162.0, 150.0, 27.0)" type="text/plain">69 73 69</text>
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<properties format="literal" node_id="13">{'controlAreaVisible': True, 'currentScriptIndex': 0, 'savedWidgetGeometry': b'\x01\xd9\xd0\xcb\x00\x03\x00\x00\x00\x00\x02(\x00\x00\x00\xbd\x00\x00\x05W\x00\x00\x03;\x00\x00\x020\x00\x00\x00\xdc\x00\x00\x05O\x00\x00\x033\x00\x00\x00\x00\x00\x00\x00\x00\x07\x80\x00\x00\x020\x00\x00\x00\xdc\x00\x00\x05O\x00\x00\x033', 'scriptLibrary': [{'name': 'Table from numpy', 'script': 'import numpy as np\nfrom Orange.data import Table, Domain, ContinuousVariable, DiscreteVariable\n\ndomain = Domain([ContinuousVariable("age"),\n ContinuousVariable("height"),\n DiscreteVariable("gender", values=("M", "F"))])\narr = np.array([\n [25, 186, 0],\n [30, 164, 1]])\nout_data = Table.from_numpy(domain, arr)\n', 'filename': None}], 'scriptText': 'import numpy as np\nfrom Orange.data import Table, Domain, ContinuousVariable, DiscreteVariable\n\ndomain = Domain([ContinuousVariable("age"),\n ContinuousVariable("height"),\n DiscreteVariable("gender", values=("M", "F"))])\narr = np.array([\n [25, 186, 0],\n [30, 164, 1]])\nout_data = Table.from_numpy(domain, arr)\n', 'splitterState': b'\x00\x00\x00\xff\x00\x00\x00\x01\x00\x00\x00\x02\x00\x00\x00\x9d\x00\x00\x00^\x01\xff\xff\xff\xff\x01\x00\x00\x00\x02\x00', 'vimModeEnabled': False, '__version__': 2}</properties>
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<properties format="literal" node_id="15">{'auto_commit': True, 'color_by_class': True, 'controlAreaVisible': True, 'dist_color_RGB': (220, 220, 220, 255), 'savedWidgetGeometry': b'\x01\xd9\xd0\xcb\x00\x03\x00\x00\x00\x00\x00\x00\x00\x00\x00\x17\x00\x00\x07\x7f\x00\x00\x04\x0f\x00\x00\x00\x00\x00\x00\x00\x17\x00\x00\x07\x7f\x00\x00\x04\x0f\x00\x00\x00\x00\x02\x00\x00\x00\x07\x80\x00\x00\x00\x00\x00\x00\x00\x17\x00\x00\x07\x7f\x00\x00\x04\x0f', 'select_rows': True, 'selected_cols': [], 'selected_rows': [], 'show_attribute_labels': True, 'show_distributions': True, '__version__': 2}</properties>
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</scheme>