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.

Table 2 Distribution of participants, 15-second electroencephalography segments, and topographic-map images in the internal data split

Data split	Class	Unique participants represented in the partition	Number of 15-second EEG segments	Topographic-map images per band	Topographic-map images across the three bands
Train	Anxiety	44	1370	6850	20550
Train	Control	43	833	4165	12495
Test	Anxiety	43	342	1710	5130
Test	Control	41	208	1040	3120
Total	Anxiety	44	1712	8560	25680
Total	Control	44	1041	5205	15615
Total	All	88	2753	13765	41295

Unique participant counts are reported separately for each partition. These counts are not additive in the fixed 80:20 analysis because different electroencephalography segments from the same participant could occur in both the training and test partitions. Therefore, the fixed analysis represents a window-level internal evaluation. Participant-grouped five-fold cross-validation with complete participant separation was used separately to estimate generalization to unseen participants within the same source dataset. EEG: Electroencephalography.

Table 3 Training configuration used in all band-specific experiments

Parameter	Value
Input size	224 × 224 × 3
Experimental bands	Alpha, beta, theta
Optimizer	Adam
Mini-batch size	64
Maximum number of epochs	12
Initial learning rate	3 × 10-5
L2 regularization	5 × 10-4
Model selection criterion	Best validation loss
Class balancing	Class-weighted classification

Table 4 Stage-wise structure of the FusedNeXt architecture

Stage	Block A (spatial-mixing)	Block B (channel-mixing)	Output channels	Transition operation	Functional role
Stem	4 × 4 Conv (stride 4), BN, GELU	-	96	- (integrated 4 × downsampling)	Initial patch embedding (224 → 56)
Stage 1	(3 × 3 DWConv → GELU → 1 × 1 GConv) ‖ (1 × 1 GConv → GELU) → ⊕ → BN	[1 × 1 GConv (× 4 expand) → GELU → 1 × 1 Conv (project)] ‖ [3 × 3 GConv → GELU → 3 × 3 GConv] → ⊕ → BN	96	2 × 2 GConv, stride 2	Low-level local topographic patterns
Stage 2	Same structure as stage 1	Same structure as stage 1	192	2 × 2 GConv, stride 2	Intermediate spatial representations
Stage 3	Same structure as stage 1	Same structure as stage 1	384	2 × 2 GConv, stride 2	High-level discriminative spatial patterns
Stage 4	Same structure as stage 1	Same structure as stage 1	768	Global average pooling	Final deep feature representation
Classification head	BN → GAP → FC (768 → 2) → Softmax	-	2	-	Anxiety/control class probabilities

Thia table shows the two-pass block A → block B sequence (block A: Spatial-mixing; block B: Channel-mixing; applied twice in succession inside each stage) and the transition operations between stages. ‖ denotes parallel paths, ⊕ denotes element-wise additive fusion, GConv denotes a grouped convolution, DWConv denotes a depthwise convolution. GELU: Gaussian Error Linear Unit; FC: Fully connected layer.

Table 5 Parameter count and computational cost of the proposed FusedNeXt model

Architectural component	Configuration	Value
Input size	Topographic map image	224 × 224 × 3
Number of stages	Hierarchical FusedNeXt blocks	4
Channel widths	Stage 1 - stage 4	96/192/384/768
Trainable parameters	Total	≈ 7.17 M
Computational cost	Forward pass per image	≈ 1.95 GFLOPs
Inference time	Per topographic map image	≈ 3 milliseconds

Table 6 Overall classification performance of the FusedNeXt model for alpha, beta, and theta bands

Band	Accuracy (%)	Balanced accuracy (%)	Macro precision (%)	Macro recall (%)	Macro specificity (%)	Macro F1-score (%)	AUC
Alpha	81.24	80.94	80.02	80.94	80.94	80.38	0.8958
Beta	84.58	84.83	83.51	84.83	84.83	83.96	0.9296
Theta	85.24	84.93	84.16	84.93	84.93	84.49	0.9297

AUC: Area under the curve.

Table 7 Class-wise performance results of the FusedNeXt model for each frequency band

Band	Class	Support	Precision (%)	Recall/sensitivity (%)	Specificity (%)	F1-score (%)
Alpha	Anxiety	1710	86.94	82.16	79.71	84.49
Alpha	Control	1040	73.10	79.71	82.16	76.26
Beta	Anxiety	1710	90.70	83.80	85.87	87.11
Beta	Control	1040	76.32	85.87	83.80	80.81
Theta	Anxiety	1710	89.66	86.20	83.65	87.90
Theta	Control	1040	78.66	83.65	86.20	81.08

Table 8 Error distribution obtained from the confusion matrices for alpha, beta, and theta bands

Band	Correct predictions	Incorrect predictions	Anxiety → control	Control → anxiety	Error rate (%)
Alpha	2234	516	305	211	18.76
Beta	2326	424	277	147	15.42
Theta	2344	406	236	170	14.76

Table 9 Training and testing time analysis of the FusedNeXt model for each frequency band

Band	Training time (seconds)	Training time (minutes)	Testing time (seconds)	Test images	Average inference time per image (milliseconds)
Alpha	633.84	10.56	8.55	2750	3.11
Beta	578.01	9.63	8.53	2750	3.10
Theta	581.93	9.70	9.14	2750	3.32

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.

Table 11 Comparison of FusedNeXt with baseline deep-learning architectures under participant-grouped five-fold cross-validation

Band	Model	Parameters (M)	GFLOPs	Window accuracy, %	Participant accuracy, %	Participant balanced accuracy, %	Participant macro F1, %	Participant AUC	95%CI for participant AUC
Alpha	Shallow CNN	1.42	0.31	72.48 ± 1.83	68.45 ± 2.64	68.37 ± 2.58	68.11 ± 2.71	0.7416 ± 0.0264	0.6468-0.8219
Alpha	ResNet-18	11.69	1.82	75.93 ± 1.47	72.31 ± 2.18	72.24 ± 2.09	71.98 ± 2.22	0.7857 ± 0.0219	0.6947-0.8586
Alpha	MobileNetV2	2.23	0.32	75.16 ± 1.61	71.80 ± 2.36	71.72 ± 2.28	71.46 ± 2.41	0.7789 ± 0.0237	0.6862-0.8537
Alpha	ConvNeXt-Tiny	27.82	4.47	76.84 ± 1.42	73.20 ± 2.04	73.13 ± 1.97	72.91 ± 2.10	0.7982 ± 0.0205	0.7086-0.8694
Alpha	FusedNeXt	7.17	1.95	78.21 ± 1.36	74.66 ± 1.92	74.58 ± 1.87	74.31 ± 1.98	0.8104 ± 0.0188	0.7237-0.8796
Beta	Shallow CNN	1.42	0.31	75.66 ± 1.72	71.20 ± 2.52	71.14 ± 2.47	70.88 ± 2.58	0.7794 ± 0.0247	0.6880-0.8532
Beta	ResNet-18	11.69	1.82	79.18 ± 1.39	75.42 ± 2.06	75.36 ± 2.01	75.11 ± 2.13	0.8281 ± 0.0198	0.7461-0.8901
Beta	MobileNetV2	2.23	0.32	78.64 ± 1.48	74.90 ± 2.21	74.83 ± 2.15	74.57 ± 2.26	0.8197 ± 0.0212	0.7356-0.8841
Beta	ConvNeXt-Tiny	27.82	4.47	80.03 ± 1.31	76.55 ± 1.93	76.50 ± 1.88	76.28 ± 1.97	0.8369 ± 0.0185	0.7568-0.8966
Beta	FusedNeXt	7.17	1.95	81.37 ± 1.24	77.84 ± 1.76	77.79 ± 1.72	77.62 ± 1.81	0.8448 ± 0.0173	0.7669-0.9028
Theta	Shallow CNN	1.42	0.31	76.42 ± 1.69	72.10 ± 2.47	72.02 ± 2.41	71.80 ± 2.52	0.7923 ± 0.0239	0.7022-0.8642
Theta	ResNet-18	11.69	1.82	80.36 ± 1.34	76.58 ± 1.98	76.51 ± 1.92	76.27 ± 2.03	0.8396 ± 0.0189	0.7600-0.8991
Theta	MobileNetV2	2.23	0.32	79.74 ± 1.43	75.62 ± 2.13	75.55 ± 2.07	75.30 ± 2.18	0.8318 ± 0.0201	0.7501-0.8934
Theta	ConvNeXt-Tiny	27.82	4.47	81.14 ± 1.26	77.90 ± 1.83	77.84 ± 1.78	77.67 ± 1.87	0.8472 ± 0.0177	0.7698-0.9049
Theta	FusedNeXt	7.17	1.95	82.48 ± 1.18	79.10 ± 1.69	79.05 ± 1.64	78.88 ± 1.73	0.8563 ± 0.0165	0.7817-0.9110

CI: Confidence interval; AUC: Area under the curve; CNN: Convolutional neural network.

Table 12 Component-level ablation analysis of the FusedNeXt architecture

Configuration	Spatial dual path	Channel dual path	Expansion × 4	Parameters (M)	GFLOPs	Alpha balanced accuracy, %	Beta balanced accuracy, %	Theta balanced accuracy, %	Mean balanced accuracy, %	Mean macro F1, %	Mean AUC
Full FusedNeXt	√	√	√	7.17	1.95	74.58 ± 1.87	77.79 ± 1.72	79.05 ± 1.64	77.14 ± 1.74	76.94 ± 1.84	0.8372 ± 0.0175
Without additional spatial path	×	√	√	6.84	1.81	72.96 ± 2.01	76.10 ± 1.86	77.28 ± 1.79	75.45 ± 1.89	75.20 ± 1.97	0.8196 ± 0.0191
Without parallel channel path	√	×	√	6.22	1.69	72.41 ± 2.09	75.32 ± 1.94	76.84 ± 1.87	74.86 ± 1.97	74.60 ± 2.05	0.8118 ± 0.0200
Without inverted-bottleneck expansion	√	√	×	3.96	1.03	73.65 ± 1.95	76.74 ± 1.79	78.12 ± 1.72	76.17 ± 1.82	75.92 ± 1.91	0.8283 ± 0.0184

AUC: Area under the curve.

Table 13 Paired statistical comparisons between frequency bands and model architectures

Comparison	Metric	Difference	95%CI of difference	Raw P value	Holm-adjusted P value	Statistical interpretation
Beta - alpha	Balanced accuracy	3.21	0.86-5.72	0.0110	0.0220	Significant
Theta - alpha	Balanced accuracy	4.47	1.98-7.18	0.0020	0.0060	Significant
Theta - beta	Balanced accuracy	1.26	-0.94 to 3.46	0.2380	0.2380	Not significant
Beta - alpha	Macro F1	3.31	0.91-5.83	0.0100	0.0200	Significant
Theta - alpha	Macro F1	4.57	2.02-7.31	0.0020	0.0060	Significant
Theta - beta	Macro F1	1.26	-1.01 to 3.51	0.2510	0.2510	Not significant
Beta - alpha	AUC	0.0344	0.0081-0.0617	0.0130	0.0260	Significant
Theta - alpha	AUC	0.0459	0.0180-0.0745	0.0030	0.0090	Significant
Theta - beta	AUC	0.0115	-0.0128 to 0.0354	0.3370	0.3370	Not significant
FusedNeXt - ConvNeXt-Tiny (beta band)	Balanced accuracy	1.29	0.21-2.41	0.0210	0.0420	Significant
FusedNeXt - ConvNeXt-Tiny (beta band)	Macro F1	1.18	0.09-2.32	0.0360	0.0720	Not significant after correction
FusedNeXt - ConvNeXt-Tiny (beta band)	AUC	0.0107	-0.0019 to 0.0234	0.0970	0.0970	Not significant

CI: Confidence interval; AUC: Area under the curve.