EEG-based efficient demographic classification with selective data approach and scalable hybrid network

The proliferation of portable and wearable electroencephalography (EEG) devices has encouraged EEG research in various areas. These devices, while convenient, often come with limited computational capabilities. However, the challenge of minimizing network complexity for such edge devices was not fully addressed in previous studies. To tackle this, a scalable hybrid network is proposed to classify EEG signals with different demographic factors on edge devices. This model blends a convolutional neural network (CNN) with a self-attention mechanism in a hybrid block structure. This design alternates between CNN layers and self-attention layers to efficiently capture both local and global features. In this study, EEG signals acquired using a portable EEG device during gaming session is classified particularly into pre-puberty and puberty stages. The developed scalable hybrid network (SH-Net) has shown promising results in distinguishing between pre-puberty and puberty EEG signals. As a result, the first stage of this model showed higher accuracy compared to other models in 10-fold cross-validation: 93.57% for four channels, 89.04% for the frontal lobe channels (AF), and 81.46% for the temporal lobe channels (TP). Notably, the third stage of this model, using the AF channel, achieved higher accuracy compared to other evaluated models that utilized four channels.