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@@ -79,14 +79,14 @@ The dataset, originally provided in **CSV format**, underwent a comprehensive pr
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* Each individual channel's EEG data was converted into **audio signals** and saved in **.wav format**, allowing the brain signals to be audibly analyzed.
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* The entire preprocessing workflow was implemented in **Python** to ensure scalability and accuracy.
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The dataset captured brainwave signals corresponding to the following activities:
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1)**BEO** (Baseline with Eyes Open): One-time recording at the beginning of each run [3].
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2)**CLH** (Closing Left Hand): Five recordings per run [3].
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3)**CRH** (Closing Right Hand): Five recordings per run [3].
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4)**DLF** (Dorsal Flexion of Left Foot): Five recordings per run [3].
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5)**PLF** (Plantar Flexion of Left Foot): Five recordings per run [3].
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6)**DRF** (Dorsal Flexion of Right Foot): Five recordings per run [3].
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7)**PRF** (Plantar Flexion of Right Foot): Five recordings per run [3].
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8)**Rest**: Recorded between each task to capture the resting state [3][4].
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1.**BEO** (Baseline with Eyes Open): One-time recording at the beginning of each run [3].
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2.**CLH** (Closing Left Hand): Five recordings per run [3].
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3.**CRH** (Closing Right Hand): Five recordings per run [3].
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4.**DLF** (Dorsal Flexion of Left Foot): Five recordings per run [3].
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5.**PLF** (Plantar Flexion of Left Foot): Five recordings per run [3].
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6.**DRF** (Dorsal Flexion of Right Foot): Five recordings per run [3].
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7.**PRF** (Plantar Flexion of Right Foot): Five recordings per run [3].
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8.**Rest**: Recorded between each task to capture the resting state [3][4].
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### 3. Feature Extraction and Classification
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Feature extraction and activity classification were performed using **transfer learning** with **YamNet**[5], a deep neural network model.
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## Protocols
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Here is the protocol(steps) to reproduce our work with ease.
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