FusedNeXt-based anxiety detection from electroencephalography topographic maps: A comparative analysis of alpha, beta, and theta bands

Table 10 Focused comparison of the present study with representative electroencephalography-based anxiety studies

Ref.	Task and cohort	EEG representation and model	Validation strategy	Best reported result	Relation to the present study
Mokatren et al[35], 2021	Binary SAD vs HC classification; 32 SAD and 32 HC participants	Wavelet-packet energy and entropy values from five frequency bands were mapped to a 15 × 15 image-like electrode representation. CNN, RBF-SVM, and kNN were evaluated	Stratified subject-independent eight-fold cross-validation	92.19% accuracy with CNN	Electrode geometry was retained in an image-like representation. However, separate alpha-, beta-, and theta-band topographic maps were not compared under identical experimental conditions
Muhammad and Al-Ahmadi[39], 2022	Two-level and four-level state-anxiety classification; DASPS dataset with 23 participants	Mean power, RASM, and asymmetry features were extracted mainly from theta and beta bands. RF, DT, kNN, SVM, and MLP were evaluated	Leave-one-participant-out evaluation	94.90% accuracy for two levels and 92.74% for four levels	The results support the relevance of theta- and beta-band information. However, topographic maps and end-to-end spatial feature learning were not used
Shen et al[27], 2022	Binary GAD vs HC classification; 45 GAD and 36 HC participants	PSD, fuzzy entropy, and PLI connectivity features were combined. SVM, RF, and BP-bagging were evaluated	Ten repetitions of an 80:20 hold-out split; participant grouping was not clearly reported	97.83% ± 0.40% accuracy and 97.95% F1-score with SVM	Spectral, nonlinear, and connectivity information was combined. However, overlapping segments and unclear participant-level separation limit direct comparison
Al-Ezzi et al[41], 2023	Four-class SAD severity assessment; 66 SAD and 22 HC participants	PDC and graph-theory features were extracted from four frequency bands. SVM, kNN, LDA, NB, and DT were evaluated	Subject-dependent ten-fold cross-validation	92.78% accuracy with SVM	Frequency-specific connectivity and network topology were evaluated. The subject-dependent protocol did not establish performance on unseen participants
Liu et al[42], 2023	Binary GAD vs HC classification; 45 GAD and 36 HC participants	Broad high-frequency EEG intervals of 4-30 Hz and 10-30 Hz were processed by an MSTCNN with squeeze-and-excitation attention	Participant-level validation was not clearly reported	99.48% accuracy for 4-30 Hz and 99.47% for 10-30 Hz	Very high results were obtained from broad frequency intervals. However, separate alpha-, beta-, and theta-band topographic maps were not evaluated
Ghonchi et al[43], 2024	Binary and four-class anxiety classification; DASPS dataset with 23 participants	Beta-band EEG was converted into temporal sequences of 11 × 11 scalp maps. A 2D CNN, squeeze-and-excitation attention, and LSTM were used	Five-fold cross-validation; participant-wise fold construction was not reported	94.24% ± 0.33% accuracy for two classes and 92.58% ± 0.52% for four classes	This study is closely related because scalp-map representations were used. However, only the beta band was examined, and participant-independent validation was not documented
Luo et al[28], 2024	Four-level GAD grading; 39 controls, 9 mild, 38 moderate, and 33 severe GAD participants	PLI connectivity features were extracted from theta, alpha1, alpha2, and beta bands. LightGBM, XGBoost, and CatBoost were evaluated	Three repetitions of five-fold cross-validation with CCR resampling; participant-wise fold construction was not reported	98.1% ± 0.6% accuracy with CatBoost	Several frequency bands were evaluated through connectivity features. However, the validation unit was unclear, resampling was used, and spatial topographic maps were not examined
Adochiei et al[44], 2026	HAM-A-based non-anxious, moderate, and severe categories; 16 in-house and 23 DASPS participants	Spectral power, entropy, Hjorth, and DWT features were extracted from eight common channels. Logistic regression, MLP, and kNN were evaluated	Nested cross-validation; preprocessing and model-selection steps were restricted to training folds	87.5% accuracy and 0.859 F1-score with MLP	A structured validation pipeline was used. However, the cohort was small and heterogeneous, and severe anxiety was underrepresented
Present study	Binary anxiety vs control classification; 44 anxiety and 44 control participants	Alpha-, beta-, and theta-band powers were separately converted into EEG topographic maps. The proposed FusedNeXt architecture was used for classification	Fixed 80:20 hold-out split at the 15-second EEG-segment level. Windows from one segment remained in the same partition, but different segments from the same participant could occur in both partitions	Theta: 85.24% accuracy, 84.93% balanced accuracy, 84.49% macro F1-score, and 0.9297 AUC	The three bands were compared with the same data split, architecture, training configuration, and test protocol. However, the results represent window-level internal estimates and do not establish subject-independent generalization

EEG: Electroencephalography; SAD: Social anxiety disorder; HC: Healthy control; CNN: Convolutional neural network; 2D: Two-dimensional; RBF: Radial basis function; SVM: Support vector machine; kNN: K-nearest neighbors; RASM: Rational asymmetry; RF: Random forest; DT: Decision tree; MLP: Multilayer perceptron; GAD: Generalized anxiety disorder; PSD: Power spectral density; PLI: Phase lag index; PDC: Partial directed coherence; LDA: Linear discriminant analysis; NB: Naive Bayes; MSTCNN: Multi-scale temporal convolutional neural network; LSTM: Long short-term memory; CCR: Combined cleaning and resampling; HAM-A: Hamilton Anxiety Rating Scale; DWT: Discrete wavelet transform; AUC: Area under the curve.