Jul 2026· Journal of Electronics Electromedical Engineering and Medical Informatics· 0 citations· 39 references
TL;DR
A comparative temporal deep learning framework for EEG-based ASD classification by evaluating principal component analysis (PCA) and multiscale principal component analysis (MS-PCA) as feature representations combined with a recurrent neural network with bidirectional long short-term memory (RNN-BiLSTM) and a temporal convolutional network with self-attention (TCN-SA).
Abstract
Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental condition whose early identification remains challenging because clinical assessment still relies heavily on behavioral observation and expert judgment. Electroencephalography (EEG) offers a noninvasive approach for capturing neural dynamics. Still, EEG-based ASD classification remains difficult because of signal nonstationarity, limited sample sizes, and the risk of subject-identity leakage. This study proposes a comparative temporal deep learning framework for EEG-based ASD classification by evaluating principal component analysis (PCA) and multiscale principal component analysis (MS-PCA) as feature representations combined with a recurrent neural network with bidirectional long short-term memory (RNN-BiLSTM) and a temporal convolutional network with self-attention (TCN-SA). Resting-state eyes-open EEG signals were acquired from only 10 participants, consisting of 5 individuals with ASD and 5 typically developing controls, using a 16-channel acquisition system. The signals were filtered using a fourth-order Butterworth band-pass filter, transformed into PCA or MS-PCA representations, segmented into 4 s windows with 50% overlap, and evaluated using subject-wise 5-fold cross-validation to reduce subject-identity leakage. The results showed that MS-PCA produced higher descriptive performance than PCA in both temporal architectures, with the strongest descriptive result obtained by the MS-PCA + TCN-SA scheme, which achieved a mean accuracy of 97.96 ± 2.37% and balanced precision, recall, F1-score, and specificity. However, the inferential comparison between PCA and MS-PCA did not reach statistical significance at the 0.05 level, and the cohort size was limited to 10 participants. Therefore, these findings should be interpreted as preliminary descriptive evidence within the present cohort rather than evidence of diagnostic readiness or robust clinical applicability. Larger, independent, and demographically diverse EEG datasets with richer clinical characterization are required to confirm the observed trend and evaluate the generalizability of the proposed framework.
The proposed electroencephalography-based ASD detection method was evaluated against previous approaches using the same datasets, showing a substantial improvement in performance, and the need to evaluate the proposed approach on larger datasets to further ensure model generalization.
B. S. Falih, Mohannad K. Sabir, A. Aydın· Brain Topography· 0 citations
It is shown that subject-level validation, preprocessing, and architectural decisions materially influence EEG-based ADHD classification, and EEG-MSCNet contributes both a model and a systematic evaluation framework.
Javier Sanchis, T. Kechadi, Miguel A. Teruel et al.· Multidimensional systems and...· 0 citations
Abstract Schizophrenia is a neuropsychiatric disorder that affects brain activity, making timely and reliable diagnosis challenging. Although electroencephalography (EEG) provides a non-invasive and cost-effective diagnostic modality, its non-stationary nature and complex temporal–spectral characteristics limit the effectiveness of conventional machine learning and deep learning approaches, which often rely on a single time–frequency representation or simple feature fusion strategies. To address these limitations, this study proposes an attention-based fusion framework that integrates complementary time–frequency representations for EEG-based schizophrenia detection. Spectrograms and scalograms generated using the Short-Time Fourier Transform (STFT) and Continuous Wavelet Transform (CWT) capture global spectral information and localized temporal–frequency characteristics, respectively. These representations are processed by a Vision Transformer (ViT), while a bidirectional cross-attention module enables effective interaction and fusion of complementary features before global feature learning. The proposed framework achieved average classification accuracies of 98.34 ± 0.21% and 98.68 ± 0.15% on Dataset 1 and Dataset 2, respectively. Comparative experiments demonstrate that the proposed framework consistently improves classification performance over methods based on individual time–frequency representations and conventional feature fusion approaches, indicating that the proposed attention-guided fusion strategy effectively exploits complementary EEG information for schizophrenia detection.
Farah Shan, Shalini Z Ninoria· Journal of Information Assur...· 0 citations
ABSTRACT. Mild cognitive impairment (MCI) is an important condition that may progress to Alzheimer disease (AD) or other types of dementia. If MCI can be detected early, timely interventions may be implemented. Traditional diagnostic methods rely on neuropsychological tests and imaging studies but have limitations in terms of efficiency and accessibility. Therefore, electroencephalogram (EEG)-based deep learning approaches represent promising developments that may offer new frameworks for the early diagnosis of MCI. Objective: This study aimed to develop a long short-term memory (LSTM)-based architecture for classifying MCI using EEG signals. The temporal characteristics of EEG signals may reveal meaningful information that improves classification performance. Methods: A publicly available EEG dataset comprising 27 subjects (11 MCI; 16 normal) was used. Raw EEG signals were preprocessed using band-pass filtering, Independent Component Analysis, and segmentation. The 64-node LSTM model was trained using processed EEG segments for binary classification. The model was evaluated using standard performance metrics. Results: The proposed LSTM-based model achieved an accuracy of 98.14% for MCI versus normal classification, outperforming conventional algorithms such as K-Nearest Neighbors (KNN) and Support Vector Machine (SVM). The model also demonstrated high precision (99.21%), recall (98.69%), and F1-score (98.95%). The confusion matrix showed 4,774 normal and 3,257 MCI segments correctly classified, with very few misclassifications, highlighting its strong discriminative capability. Conclusion: This study highlights the potential of LSTM networks for early MCI detection. The findings suggest that deep learning-driven EEG analysis may be a valuable tool for noninvasive and scalable cognitive health assessments.
H. Ohal, Shamla Mantri· Dementia & Neuropsychologia· 0 citations
Findings indicate that the learned brain network representations capture distinct and quantifiable abnormalities in ASD-related neural dynamics, establishing the proposed framework as a robust, interpretable, and measurement-aligned tool for objective ASD assessment.
Madhuparna Das, Poulomi Pal, M. Mahadevappa· IEEE Transactions on Instrum...· 0 citations
Parkinson’s disease (PD) is associated with motor impairment and altered cortical dynamics. Although resting-state electroencephalography (EEG) has been widely studied for PD classification, walking EEG remains comparatively underexplored despite its relevance to gait dysfunction. This study developed an EEG-only, leakage-free framework for participant-level classification of PD and healthy controls (HCs) using resting- and walking-state EEG data from the OpenNeuro ds007526 dataset. Following selection, preprocessing, and quality control, 132 participants were retained, including 109 individuals with PD and 23 HCs. EEG recordings were resampled to 128 Hz, filtered between 0.5 and 40 Hz, screened for artifacts, harmonized across conditions, and segmented into overlapping 6 s windows. Classical machine-learning (ML) models used participant-level engineered features, whereas deep-learning (DL) models classified pooled resting-state and walking windows. Extra Trees achieved the highest balanced accuracy (0.71) among the ML, while ShallowConvNet-Lite was the strongest standard DL baseline (0.82). The proposed NeuroAtten-PD model achieved an accuracy of 0.86, a balanced accuracy of 0.84, an F1-score of 0.91, an ROC-AUC of 0.90, and a sensitivity of 0.88. After Holm correction, participant-level paired comparisons indicated significant differences from DeepConvNet-Lite and EEGNet-Lite. These findings demonstrate the feasibility of participant-level PD classification using a pooled collection of resting-state and walking EEG windows. With validation in larger, independent, and more balanced clinical cohorts, the proposed framework could support objective EEG-based decision support and provide a foundation for portable or wearable systems for longitudinal monitoring of PD-related cortical changes in clinical and home-based settings.
H. Herath, Prathiksha Padmanabha, Nuwan Madusanka Vithanage et al.· Applied Sciences· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
