The article explores the classification of electroencephalographic signals through their time-frequency representations using adapted convolutional neural networks. The study shows that targeted architectural modifications significantly enhance the performance of automated electroencephalography (EEG) analysis. This reflects the principle “the signal shapes the network,” emphasizing that model design must align with the spectral features and rhythm dynamics of neural activity. EEG signals were converted into spectrogram-like time-frequency maps and processed as image inputs for training. To match the data properties, classical convolutional neural network (CNN) architectures were reconfigured: some streamlined for efficiency, others tailored to single-channel input, and others extended with residual blocks and advanced regularization. Experimental evaluation confirmed consistent learning stability across all variants, with specific advantages for each: LiteResNet2D provided the highest accuracy, LiteMobileNet2D offered optimal performance for low-resource environments, and SimpleAlexNet2D facilitated prototyping and rapid exploration. The results underline the necessity of adapting convolutional architectures to EEG properties as a foundation for robust, generalizable classification systems. This strategy enables scalable tools suitable for applications in neuroscience research, brain–computer interfaces, and clinical diagnostics.
