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

Table 1 Comparison of selected electroencephalography-based studies on anxiety detection and severity assessment

Ref.	Task and cohort	EEG setting and representation	Model	Validation strategy	Best reported result	Relevance and main limitation
Baghdadi et al[34], 2019	Two-level and four-level anxious-state classification; DASPS dataset with 23 participants	EEG was recorded with an Emotiv EPOC system during anxiety-inducing psychological stimulation. Handcrafted time-, frequency-, and nonlinear-domain features were used	Stacked sparse autoencoder and conventional classifiers	The participant-level validation protocol was not clearly reported	83.50% accuracy for two levels and 74.60% for four levels	The study introduced the DASPS dataset and evaluated several EEG features. However, the cohort was small, and spatial topographic-map learning was not used
Chen et al[10], 2021	Anxious-state classification in a closed neurofeedback setting	Frontal frequency-domain EEG features were extracted in an affective BCI-based neurofeedback paradigm	RBF-SVM with a one-vs-one strategy	The participant-level validation procedure was not clearly described	92% accuracy	The study addressed anxiety in a specific neurofeedback paradigm. It did not use band-specific topographic maps or deep spatial learning
Mokatren et al[35], 2021	Binary SAD vs HC classification; 32 SAD and 32 HC participants	Approximately 4 minutes of resting-state EEG; 34 channels; 1024 Hz. Wavelet-packet energy and entropy from five frequency bands were mapped to a 15 × 15 image-like representation	CNN, RBF-SVM, and kNN	Stratified subject-independent eight-fold cross-validation; eight participants were used for testing in each fold	92.19% accuracy with CNN and the 2D image representation	Electrode geometry was preserved in an image-like structure. However, the individual contribution of alpha, beta, and theta topographic maps was not assessed
Shikha et al[36], 2021	Anxiety classification; DASPS dataset with 23 participants	Time-, frequency-, and time-frequency-domain EEG features were extracted and subjected to feature selection	Stacked sparse autoencoder and conventional machine-learning classifiers	The participant-level validation strategy was not clearly reported	83.93% accuracy with the stacked sparse autoencoder	The study evaluated complementary handcrafted features but did not use topographic maps or end-to-end spatial feature learning
Al-Ezzi et al[37], 2021	Four-class SAD severity assessment; severe, moderate, mild, and HC groups with 22 participants per group	Six-minute eyes-closed resting-state EEG; 32 channels; 2048 Hz, downsampled to 256 Hz. PDC-based effective-connectivity matrices were produced for five frequency ranges	CNN, LSTM, and CNN-LSTM	Ten-fold cross-validation was reported. An additional 60-subject training and 28-subject testing analysis was presented	93% average accuracy, 95% sensitivity, and 85% specificity with CNN-LSTM	Clinical SAD severity was assessed through effective connectivity. However, no external cohort was used, and the reported validation procedures were not fully uniform
Li et al[38], 2022	Recognition of four anxiety levels	Comprehensive EEG features were extracted. Beta-band activity and frontal regions were reported as important	SVM	Participant-level fold construction was not clearly described	62.56% accuracy	The study examined multiple anxiety levels but relied on handcrafted EEG features rather than image-based spatial representations
Muhammad and Al-Ahmadi[39], 2022	Two-level and four-level state-anxiety classification; DASPS dataset with 23 participants	Channel-based mean power, RASM, and asymmetry features were extracted, mainly from theta and beta bands	RF, DT, kNN, SVM, and MLP	Leave-one-participant-out evaluation; samples from the test participant were excluded from training	94.90% accuracy for two levels and 92.74% for four levels with RF	A participant-separated protocol and band-related features were used. However, topographic-map representation and deep spatial learning were not evaluated
Al-Ezzi et al[40], 2022	Four-class SAD severity classification; 22 severe, 22 moderate, 22 mild, and 22 HC participants	Fuzzy entropy features were extracted from delta, theta, alpha, and beta bands	NB and other machine-learning classifiers	The participant-level validation procedure was not clearly documented	86.93% accuracy, 92.46% sensitivity, and 95.32% specificity	Nonlinear EEG complexity was evaluated across several bands. The approach did not preserve electrode topology or use image-based learning
Shen et al[27], 2022	Binary GAD vs HC classification; 45 GAD and 36 HC participants	Ten-minute eyes-closed resting-state EEG; 16 channels; 250 Hz. Four-second segments with 50% overlap were represented by PSD, fuzzy entropy, and PLI connectivity features	SVM, RF, and BP-bagging	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 features were combined. Overlapping segments and unclear participant-level separation restrict direct comparison
Al-Ezzi et al[41], 2023	Four-class SAD severity assessment; 66 SAD and 22 HC participants	Four-to-six-minute eyes-closed resting-state EEG; 32 channels; 2048 Hz, downsampled to 256 Hz. PDC and graph-theory features were extracted from four bands	SVM, kNN, LDA, NB, and DT	Explicitly reported subject-dependent ten-fold cross-validation	92.78% accuracy, 95.25% sensitivity, and 94.12% specificity with SVM	Directed connectivity and network topology were assessed. Subject-dependent validation limits evidence for unseen-participant generalization
Liu et al[42], 2023	Binary GAD vs HC classification; 45 GAD and 36 HC participants	Ten-minute resting-state EEG. High-frequency representations covering 4-30 Hz and 10-30 Hz were evaluated	MSTCNN with squeeze-and-excitation attention	The participant-level validation protocol was not clearly reported	99.48% accuracy for 4-30 Hz and 99.47% for 10-30 Hz	Very high performance was reported for broad high-frequency EEG intervals. Controlled, separate alpha-, beta-, and theta-map comparisons were not performed
Ghonchi et al[43], 2024	Binary normal vs anxious classification and four-class normal, light, moderate, and severe classification; DASPS dataset with 23 participants	Six anxiety-induction trials per participant; 14 electrodes. Beta-band EEG was transformed into sequences of 11 × 11 scalp maps	2D CNN, squeeze-and-excitation attention, and LSTM	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 spatiotemporal scalp maps were used. However, only the beta band was evaluated, 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	Ten-minute eyes-closed resting-state EEG; 16 channels; 250 Hz. PLI connectivity features were extracted from theta, alpha1, alpha2, and beta bands	LightGBM, XGBoost, and CatBoost	Three repetitions of five-fold cross-validation with CCR resampling. Participant-wise fold construction was not reported	98.1% ± 0.6% accuracy with CatBoost	A clinically relevant severity task was addressed. However, resampling was used, the validation unit was unclear, and the representation was connectivity-based rather than topographic
Adochiei et al[44], 2026	HAM-A-based non-anxious, moderate, and severe categories; 16 in-house participants and 23 DASPS participants	Data from two EEG systems were harmonized to eight common channels. Spectral power, entropy, Hjorth, and DWT features were extracted	Logistic regression, MLP, and kNN	Nested cross-validation; five-fold inner model selection. Preprocessing, feature selection, PCA, scaling, and SMOTE were restricted to training folds	87.5% accuracy and 0.859 F1-score with MLP; no severe case was correctly identified	A portable-EEG setting and a structured validation pipeline were used. However, the sample was small, two data sources were combined, and severe anxiety was substantially underrepresented

The studies differ substantially in clinical targets, participant cohorts, electroencephalography systems, segmentation procedures, representations, and validation protocols. Their numerical results should therefore not be interpreted as a direct ranking of model performance. EEG: Electroencephalography; BCI: Brain-computer interface; RBF: Radial basis function; SVM: Support vector machine; SAD: Social anxiety disorder; HC: Healthy control; CNN: Convolutional neural network; kNN: K-nearest neighbors; RASM: Rational asymmetry; LSTM: Long short-term memory; PDC: Partial directed coherence; PSD: Power spectral density; RF: Random forest; DT: Decision tree; MLP: Multilayer perceptron; NB: Naive Bayes; MSTCNN: Multi-scale temporal convolutional neural network; GAD: Generalized anxiety disorder; PLI: Phase lag index; LDA: Linear discriminant analysis; CCR: Combined cleaning and resampling; HAM-A: Hamilton Anxiety Rating Scale; DWT: Discrete wavelet transform; 2D: Two-dimensional.