AI-Based Analysis and Modeling of Brainwave Data:

In the Hypnus project, the use of artificial intelligence (AI) technology for analyzing and modeling brainwave data is crucial, as it effectively manages the complexity of brainwave data, supports real-time prediction, offers personalized adaptation, enhances generalization capabilities, reduces noise interference, and facilitates complex task execution. AI technology enables Hypnus not only to extract valuable information accurately from high-dimensional, nonlinear brainwave data but also to adapt to individual differences and environmental changes, significantly improving Hypnus's performance and applicability. This allows Hypnus to develop corresponding algorithms and applications based on the collected brainwave data, which can be applied across various fields such as medical, assistive technologies, and entertainment.

The model proposed by Hypnus uses convolutional layers to extract low-level features, introduces non-linear characteristics through batch normalization layers and LeakyReLU activation functions (Leaky Rectified Linear Unit), and extracts overall features in pooling layers based on averaging methods. The final classification and prediction are performed through fully connected layers. To enhance the model's performance, Hypnus has researched and adopted methods from VGGNet (Visual Geometry Group Network), using small convolutional kernels in convolutional layers to increase channel numbers and reduce the feature map dimensions in pooling layers, thereby creating a computationally efficient model with reduced costs. The model's architecture is outlined as follows:

Compared to the VGG network, the model proposed by Hypnus has improvements in network structure, padding, batch normalization layers, and activation functions, making the entire network more streamlined and efficient, specifically suited for processing EEG signals. Specific details include:

Network Structure: The VGG network has a deep network structure with multiple repeated convolutional blocks, which results in a large number of parameters. Hypnus, however, uses fewer convolutional and fully connected layers, making the network more compact.

Padding: Unlike the VGG network, which does not employ padding and thus each convolutional layer reduces the input size, limiting the model's expressive capability, Hypnus uses padding in every convolutional layer to accommodate EEG signal processing.

Batch Normalization Layers: The VGG network does not use batch normalization layers, which can lead to difficulties in training and overfitting. Hypnus incorporates batch normalization layers after each convolutional layer to speed up convergence and improve the model's generalization capability.

Activation Functions: Compared to the ReLU activation function used in the VGG network, the LeakyReLU activation function employed by Hypnus allows a small negative slope when the input is less than zero, broadening the activation function's nonlinear expressive capacity.

This allows the network to better capture the nonlinear characteristics of EEG signals, thereby enhancing the model's performance and expressive power.