EEG-Based Mental State Classification Using Statistical Features and Convolutional Neural Networks
Keywords:
convolutional neural network, classification, electroencephalogram, mental state, statistical dataAbstract
The current research examines the effectiveness of Convolutional Neural Networks (CNNs) in classifying mental states from EEG data through statistical features. Conventional EEG classification frequently depends on raw signals or picture transformations, which may insufficiently utilize feature-level differentiations. This research presents a direct statistical feature-based CNN methodology to enhance classification accuracy while minimizing computational complexity. Electroencephalogram data were obtained from 10 subjects utilizing the Muse Headband, resulting in 200 samples (100 in each category). To reduce training instability resulting from the constrained dataset, augmentation approaches expanded the sample size to 1000. The optimized CNN model (32 units, learning rate 0.01, 90 epochs) attained 98% accuracy using enhanced data, illustrating the effectiveness of augmentation in stabilizing training. Comparative analysis with prior studies validates that CNNs can effectively categorize statistical EEG data; however, subsequent research should investigate hybrid architectures (CNN-GRU, Transformers) to enhance temporal modeling. These findings enhance EEG-based mental state classification by demonstrating the application of statistical features in deep learning models and highlighting the significance of dataset size and augmentation.
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