FusedNeXt-based anxiety detection from electroencephalography topographic maps: A comparative analysis of alpha, beta, and theta bands
Year: 2026
Type: article
Abstract: BACKGROUND Electroencephalography (EEG) topographic maps preserve the spatial arrangement of scalp electrodes, but their apparent classification performance is sensitive to clinical phenotyping, preprocessing, and data partitioning. AIM To evaluate whether alpha-, beta-, and theta-band topographic maps provide different classification profiles under a controlled FusedNeXt framework. METHODS A lightweight FusedNeXt architecture was evaluated using band-specific topographic maps generated from a retrospective, de-identified hospital EEG dataset. Each 15-second EEG segment was divided into five non-overlapping 3-second windows. The same architecture, training settings, and band-specific input construction were used across all experiments. The dataset comprised 44 participants labelled anxiety and 44 labelled control. Group labels were obtained from the available hospital clinical records and clinical assessment. Uniform individual DSM/ICD codes, the specific diagnostic-manual version, structured diagnostic interview records, validated anxiety-scale scores and cut-off values, and anxiety-subtype information were not retained in the de-identified dataset. An approximately 80:20 stratified split was applied at the 15-second segment level, so different segments from the same participant could occur in both partitions. Generalization to unseen participants was additionally evaluated with participant-grouped five-fold cross-validation and participant-level mean-probability aggregation. RESULTS Under the fixed segment-level split, theta achieved the numerically highest window-level accuracy (85.24%), balanced accuracy (84.93%), macro F1-score (84.49%), and area under the curve (AUC) (0.9297); beta achieved 84.58% accuracy and an AUC of 0.9296, and alpha achieved 81.24% accuracy and an AUC of 0.8958. Under participant-grouped five-fold cross-validation, participant-level accuracy was 79.10% for theta, 77.84% for beta, and 74.66% for alpha. Beta and theta significantly outperformed alpha for balanced accuracy, macro F1-score, and AUC after Holm correction, whereas theta and beta did not differ significantly. Same-dataset baseline and ablation analyses provided additional evidence on model performance and component contribution. FusedNeXt contained approximately 7.17 million trainable parameters, required approximately 1.95 GFLOPs per forward pass, and classified one image in approximately 3 milliseconds. CONCLUSION The participant-grouped analysis provides an internal estimate of generalization to unseen participants within the same source dataset. The findings support band-specific discrimination within this cohort but do not establish diagnostic utility, a validated EEG biomarker, or external clinical validity. Independent external validation and more complete clinical phenotyping remain necessary.
Source: World Journal of Psychiatry
DOI: 10.5498/wjp.123156
Institutions: Elazığ Eğitim ve Araştırma Hastanesi, Fırat University
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: diamond
OpenAlex ID: W7208713151