Performance Evaluation of Hybrid CNN-Transformer Architectures for EEG-Based Data

Archana Subhash Savadkar; Yogini Borole; Archana Shital kadam

doi:10.36948/ijfmr.2026.v08i05.87030

Performance Evaluation of Hybrid CNN-Transformer Architectures for EEG-Based Data

Author(s)	Ms. Archana Subhash Savadkar, Dr. Yogini Borole, Ms. Archana Shital kadam
Country	India
Abstract	Electroencephalography (EEG) is widely used in analysing the emotions of humans. EEGs are also widely used in brain-computer interface (BCI), clinical diagnostic, and cognitive-monitoring applications etc. There are various deep learning approaches for decoding EEG signals.CNNs focus on learning local spatial and frequency-domain characteristics of EEG signals, whereas Transformer architectures capture broader temporal dependencies using self-attention mechanisms. This paper reviews recent literature that evaluates and compares the performance of CNN-based, Transformer-based, and hybrid CNN-Transformer models across EEG applications including motor imagery classification, emotion recognition, sleep-stage scoring, seizure detection, clinical abnormality screening, and inner-speech decoding. This study evaluated hybrid CNN-Transformer architectures—specifically the cnn_temporal_transformer and dual_spatial_temporal_transformer models—for EEG-based emotion recognition across the DEAP and SEED datasets, spanning arousal, valence, and multi-class emotion classification tasks. The results demonstrate that while hybrid architectures combining local spatial-temporal feature extraction with global attention mechanisms show promise, their performance remains inconsistent across tasks and datasets, with validation accuracies ranging from 37.33% to 66.67% .
Keywords	EEG, Electroencephalography, Convolutional Neural Network, CNN, Transformer, Self-Attention, Deep Learning, Brain-Computer Interface, Performance Evaluation
Field	Engineering
Published In	Volume 8, Issue 5, September-October 2026
Published On	2026-09-06
DOI	https://doi.org/10.36948/ijfmr.2026.v08i05.87030

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Performance Evaluation of Hybrid CNN-Transformer Architectures for EEG-Based Data

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