InceptSADNet: Inception-based Stress and Affect Detection Network

A hybrid CNN-Transformer architecture for multi-class EEG-based stress classification using multi-scale temporal feature extraction, squeeze-and-excitation attention, and self-attention mechanisms.

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Table of Contents

• Overview
• Architecture
  • High-Level Pipeline
  • Block 1: Multi-Scale Temporal Convolution
  • Block 2: Depthwise Spatial Convolution
  • Block 3: Squeeze-and-Excitation (SE) Attention
  • Block 4: Channel Projection
  • Block 5: Transformer Encoder
  • Block 6: Classification Head
• Training Configuration
• Dataset
• Results
  • Intra-Subject Evaluation
  • Cross-Subject (LOSO) Evaluation
• Comparison with Baselines
  • Intra-Subject Comparison
  • Cross-Subject Comparison
• Setup and Reproduction
• Citation
• License

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Overview

InceptSADNet extends the conventional single-scale temporal convolution paradigm used in EEG classification models (such as EEGNet and ShallowConvNet) by introducing an Inception-inspired multi-scale temporal front-end coupled with a Transformer encoder for long-range temporal dependency modeling. The architecture is designed for three-class stress classification (Low / Medium / High stress) from 30-channel EEG recordings sampled at 512 Hz.

Key Design Principles

1. Multi-Scale Temporal Sensitivity: Three parallel temporal convolution branches with kernel sizes of 15, 31, and 63 samples capture oscillatory patterns at different frequency resolutions (~34 Hz, ~16 Hz, and ~8 Hz effective bandwidths respectively).
2. Channel Recalibration via SE Attention: A Squeeze-and-Excitation block dynamically reweights the importance of feature channels after spatial filtering, suppressing noisy or irrelevant frequency bands.
3. Self-Attention for Temporal Dependencies: A 3-layer Transformer encoder with multi-head self-attention captures long-range temporal correlations across the sequence of embedded EEG patches.
4. Gentle Funnel Classification: A progressively narrowing fully-connected head (2480 → 512 → 64 → 3) preserves the rich feature representation from the Transformer while providing sufficient regularization for generalization.

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Architecture

High-Level Pipeline

Raw EEG Input [B, 1001, 30]
        │
        ▼
┌─────────────────────────────────────────────────┐
│           Multi-Scale Temporal Convolution       │
│  ┌──────────┐  ┌──────────┐  ┌──────────┐      │
│  │ Conv1D   │  │ Conv1D   │  │ Conv1D   │      │
│  │ k=15     │  │ k=31     │  │ k=63     │      │
│  │ F1=8     │  │ F1=8     │  │ F1=8     │      │
│  └────┬─────┘  └────┬─────┘  └────┬─────┘      │
│       └──────────┬───┴───┬─────────┘             │
│              Concatenate (24 filters)            │
└─────────────────────┬───────────────────────────┘
                      ▼
┌─────────────────────────────────────────────────┐
│        Depthwise Spatial Convolution             │
│     Conv2D (24→48, kernel=30×1, groups=24)       │
│     BatchNorm2D → ELU → AvgPool → Dropout       │
└─────────────────────┬───────────────────────────┘
                      ▼
┌─────────────────────────────────────────────────┐
│        Squeeze-and-Excitation Block              │
│     AdaptiveAvgPool → FC(48→3) → ReLU            │
│     → FC(3→48) → Sigmoid → Channel Scaling       │
└─────────────────────┬───────────────────────────┘
                      ▼
┌─────────────────────────────────────────────────┐
│           1×1 Channel Projection                 │
│     Conv2D (48→40) → BatchNorm → ELU             │
│     → AvgPool(1,4) → Dropout → Rearrange         │
│     Output: [B, 62, 40]                          │
└─────────────────────┬───────────────────────────┘
                      ▼
┌─────────────────────────────────────────────────┐
│        Transformer Encoder (×3 layers)           │
│  ┌───────────────────────────────────────┐      │
│  │  LayerNorm → MultiHead Attention (5h) │      │
│  │  → Dropout → Residual Add             │      │
│  ├───────────────────────────────────────┤      │
│  │  LayerNorm → FFN (40→160→40, GELU)    │      │
│  │  → Dropout → Residual Add             │      │
│  └───────────────────────────────────────┘      │
│     Output: [B, 62, 40]                          │
└─────────────────────┬───────────────────────────┘
                      ▼
┌─────────────────────────────────────────────────┐
│         Classification Head (Funnel)             │
│     Flatten [B, 2480]                            │
│     → LayerNorm → Linear(2480→512) → ELU         │
│     → Dropout(0.5) → Linear(512→64) → ELU        │
│     → Dropout(0.3) → Linear(64→3)                │
└─────────────────────┬───────────────────────────┘
                      ▼
              Output: [B, 3] (logits)

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Block 1: Multi-Scale Temporal Convolution

Purpose: Extract temporal features at multiple frequency resolutions from the raw EEG signal simultaneously, inspired by the Inception module from GoogLeNet.

Motivation: EEG signals contain stress-relevant oscillatory patterns across a wide frequency spectrum (theta: 4–8 Hz, alpha: 8–13 Hz, beta: 13–30 Hz, gamma: 30–45 Hz). A single convolutional kernel captures only a narrow frequency band. By deploying three parallel branches with different receptive fields, the model simultaneously captures:

Branch	Kernel Size	Effective Receptive Field	Target Band
Branch 1	(1, 15)	~29 ms at 512 Hz	High-beta / Gamma (~34 Hz)
Branch 2	(1, 31)	~60 ms at 512 Hz	Beta (~16 Hz)
Branch 3	(1, 63)	~123 ms at 512 Hz	Alpha / Theta (~8 Hz)

Implementation: Each branch consists of:

Conv2D(1, 8, kernel=(1, k), padding=(0, k//2), bias=False)
→ BatchNorm2D(8)
→ ELU()

The outputs of all three branches are concatenated along the channel dimension, producing a tensor of shape [B, 24, 30, 1001] where 24 = 8 × 3 total feature maps.

Design Choice — BatchNorm over InstanceNorm: BatchNorm was selected over InstanceNorm after empirical evaluation. While InstanceNorm normalizes per-sample (potentially beneficial for cross-subject generalization), it was found to produce less stable gradient flow in this architecture. BatchNorm, which normalizes across the batch, provides more stable global statistics that anchor the multi-scale features to a consistent distribution.

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Block 2: Depthwise Spatial Convolution

Purpose: Learn spatial filters that combine information across all 30 EEG electrode channels for each temporal feature map independently.

Motivation: After temporal filtering, each of the 24 feature maps still spans all 30 EEG channels. The spatial convolution learns which electrodes contribute most to each temporal feature—essentially performing a data-driven spatial filter analogous to a Common Spatial Pattern (CSP) filter in classical BCI.

Implementation:

Conv2D(24, 48, kernel=(30, 1), groups=24, bias=False)
→ BatchNorm2D(48)
→ ELU()
→ AvgPool2D(1, 4)
→ Dropout(0.5)

Key Details:

• groups=24: This performs a depthwise separable convolution. Each of the 24 input channels is convolved independently with its own set of spatial filters (depth multiplier D=2), producing 24 × 2 = 48 output channels. This is significantly more parameter-efficient than a standard convolution and prevents cross-contamination between temporal scales during spatial filtering.
• kernel=(30, 1): The spatial kernel spans all 30 EEG channels, collapsing the spatial dimension to 1.
• AvgPool2D(1, 4): Downsamples the temporal dimension by a factor of 4, reducing computational cost for downstream layers.

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Block 3: Squeeze-and-Excitation (SE) Attention

Purpose: Recalibrate channel-wise feature responses by explicitly modeling inter-dependencies between the 48 feature channels.

Motivation: Not all 48 feature channels (representing different combinations of temporal scale × spatial filter) are equally informative for stress classification. The SE block learns to amplify channels that carry discriminative stress-related information and suppress noisy or redundant channels. This is particularly critical for cross-subject evaluation, where the relative importance of frequency bands varies across individuals.

Implementation:

AdaptiveAvgPool2D(1)           → Global channel descriptor [B, 48, 1, 1]
→ Flatten                      → [B, 48]
→ Linear(48, 3, bias=False)    → Squeeze (reduction ratio r=16)
→ ReLU()
→ Linear(3, 48, bias=False)    → Excitation
→ Sigmoid()                    → Channel attention weights [B, 48, 1, 1]
→ Element-wise multiplication  → Recalibrated feature map

Design Choice — SE over CBAM: While CBAM (Convolutional Block Attention Module) provides both channel and spatial attention, empirical evaluation showed that the simpler SE block provided more stable training dynamics for cross-subject EEG classification. The additional spatial attention in CBAM introduced noise when transferring across subjects with different electrode impedance profiles.

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Block 4: Channel Projection

Purpose: Reduce the channel dimensionality from 48 to the Transformer embedding size (40) and reshape the 2D feature map into a 1D sequence suitable for the Transformer encoder.

Implementation:

Conv2D(48, 40, kernel=(1, 1), bias=False)  → Pointwise projection
→ BatchNorm2D(40)
→ ELU()
→ AvgPool2D(1, 4)                          → Further temporal downsampling
→ Dropout(0.5)
→ Rearrange('b e h w → b (h w) e')         → [B, 62, 40] sequence

Output Shape: [B, 62, 40] — a sequence of 62 temporal tokens, each with an embedding dimension of 40. This serves as the input to the Transformer encoder, where each token represents a ~32 ms temporal window of multi-scale EEG features.

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Block 5: Transformer Encoder

Purpose: Model long-range temporal dependencies between the 62 embedded EEG tokens using self-attention, enabling the network to learn complex temporal patterns such as phase coupling and cross-frequency interactions that are inaccessible to purely convolutional architectures.

Architecture: The encoder consists of 3 identical layers, each containing:

5a. Multi-Head Self-Attention (MHSA)

LayerNorm(40)
→ MultiHeadAttention(embed=40, heads=5, dropout=0.5)
→ Dropout(0.5)
→ Residual Connection (+ input)

• 5 attention heads with d_k = d_v = 40 / 5 = 8 dimensions per head.
• Attention is computed as: Attention(Q, K, V) = softmax(QK^T / √d_k) · V
• Each head independently learns different temporal attention patterns (e.g., one head may attend to adjacent tokens for local patterns, while another attends to distant tokens for long-range coupling).

5b. Position-wise Feed-Forward Network (FFN)

LayerNorm(40)
→ Linear(40, 160)    → Expansion factor = 4×
→ GELU()
→ Dropout(0.5)
→ Linear(160, 40)    → Project back to embedding size
→ Dropout(0.5)
→ Residual Connection (+ input)

• GELU activation is used over ReLU following the convention of modern Transformer architectures (BERT, ViT), as it provides smoother gradients near zero.
• The 4× expansion in the hidden dimension allows the FFN to learn non-linear feature transformations while the residual connection ensures gradient flow.

Design Rationale: The Pre-Norm architecture (LayerNorm before attention/FFN rather than after) was chosen as it provides more stable training dynamics, particularly important for the relatively small EEG datasets.

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Block 6: Classification Head (Gentle Funnel)

Purpose: Map the rich 2480-dimensional feature representation from the Transformer to the final 3-class stress prediction.

Implementation:

Flatten [B, 62, 40] → [B, 2480]
→ LayerNorm(2480)
→ Linear(2480, 512) → ELU() → Dropout(0.5)
→ Linear(512, 64)   → ELU() → Dropout(0.3)
→ Linear(64, 3)

Design Choice — Funnel vs. Global Average Pooling: A common alternative is to use Global Average Pooling (GAP) over the sequence dimension, reducing [B, 62, 40] to [B, 40] before a single linear layer. While GAP is more regularized, it discards the sequential ordering information learned by the Transformer. The funnel approach preserves the positional information encoded in the flattened sequence while providing progressive dimensionality reduction through two bottleneck layers.

Regularization Strategy:

• LayerNorm before the first linear layer stabilizes the large 2480-dimensional input, preventing gradient explosion.
• Progressive dropout (0.5 → 0.3) provides stronger regularization in the wider layers where overfitting risk is highest, and relaxes regularization in narrower layers to preserve learned representations.

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Training Configuration

Parameter	Value
Optimizer	Adam
Learning Rate	2 × 10⁻³
Batch Size	128
Epochs	100
Loss Function	Cross-Entropy
Dropout (Conv)	0.5
Dropout (Head Layer 1)	0.5
Dropout (Head Layer 2)	0.3
Weight Initialization	Xavier Normal
Device	NVIDIA GV100 (32 GB VRAM)

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Dataset

The model is evaluated on the SADNet EEG Stress Dataset, consisting of 30-channel EEG recordings from 16 subjects performing cognitive tasks under varying stress levels. Each trial is a 1001-sample segment (~2 seconds at 512 Hz), labeled as one of three stress classes.

Property	Value
Subjects	16
Channels	30
Sampling Rate	512 Hz
Trial Length	1001 samples (~1.95 s)
Classes	3 (Low / Medium / High Stress)
Compressed Size	~15.2 GB (ZIP archive)

Subject IDs

S01, S05, S09, S13, S22, S31, S35, S40, S41, S42, S43, S44, S45, S49, S50, S53

Note: The raw dataset is distributed as a ~15.2 GB compressed ZIP archive containing per-subject EEG recordings in .npy format. After extraction, the dataset occupies approximately 18–20 GB on disk. Each subject folder contains pre-segmented trial data split into training, validation, and test partitions.

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Results

Intra-Subject Evaluation

In the intra-subject paradigm, the model is trained and tested on data from the same subject using a stratified train/validation/test split with oversampling to handle class imbalance.

Subject	F1 Score (%)	AUC (%)
S01	89.28	96.05
S05	74.23	90.26
S09	90.60	96.28
S13	74.53	81.95
S22	84.02	95.16
S31	94.77	95.46
S35	82.05	91.10
S40	97.25	97.74
S41	89.24	95.34
S42	91.71	93.58
S43	85.51	93.22
S44	86.37	93.90
S45	91.09	95.78
S49	84.13	96.10
S50	83.11	92.52
S53	80.65	89.82
Average	86.16	93.39
Std Dev	±6.36	±3.90

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Cross-Subject (LOSO) Evaluation

In the cross-subject paradigm, a Leave-One-Subject-Out (LOSO) protocol is used: the model is trained on data from all subjects except one, and tested on the held-out subject. This evaluates the model's ability to generalize across individuals.

Subject	F1 Score (%)	AUC (%)
S01	59.70	68.82
S05	61.44	69.28
S09	60.94	69.01
S13	59.70	67.68
S22	61.22	69.13
S31	60.89	69.64
S35	59.77	68.19
S40	58.67	66.51
S41	57.58	67.25
S42	60.90	69.62
S43	59.12	68.47
S44	62.49	68.64
S45	60.34	67.49
S49	57.97	67.78
S50	59.14	68.01
S53	60.15	68.59
Average	60.00	68.38
Std Dev	±1.23	±0.87

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Comparison with Baselines

Intra-Subject Comparison

Performance comparison with established EEG classification architectures under identical intra-subject evaluation protocols.

Model	Avg F1 (%)	Avg AUC (%)
EEGNet	73.22	82.82
EEGInception	81.04	90.16
ShallowConvNet	87.64	93.74
InterpretableCNN	82.46	91.69
InceptSADNet (Ours)	86.16	93.39

Detailed Intra-Subject Comparison by Subject

Subject	EEGNet	EEGInception	ShallowConvNet	InterpretableCNN	InceptSADNet
	F1 / AUC	F1 / AUC	F1 / AUC	F1 / AUC	F1 / AUC
S01	66.60 / 78.76	81.48 / 90.10	89.34 / 94.72	85.13 / 94.40	89.28 / 96.05
S05	59.91 / 75.04	70.03 / 85.75	75.40 / 87.51	72.81 / 86.66	74.23 / 90.26
S09	75.60 / 83.32	78.05 / 85.91	87.30 / 95.23	75.06 / 91.55	90.60 / 96.28
S13	80.51 / 87.55	77.35 / 87.76	83.22 / 90.86	56.42 / 63.06	74.53 / 81.95
S22	60.76 / 70.30	72.05 / 85.25	80.80 / 88.66	81.73 / 90.29	84.02 / 95.16
S31	77.21 / 82.92	90.00 / 93.52	91.98 / 95.68	92.68 / 97.51	94.77 / 95.46
S35	73.91 / 85.08	85.00 / 92.39	87.66 / 95.29	85.75 / 93.91	82.05 / 91.10
S40	88.53 / 90.90	93.71 / 96.41	99.09 / 98.58	95.73 / 98.27	97.25 / 97.74
S41	74.56 / 84.38	85.98 / 92.47	90.94 / 94.98	85.75 / 94.91	89.24 / 95.34
S42	80.21 / 86.77	87.95 / 91.91	89.65 / 94.54	90.63 / 96.20	91.71 / 93.58
S43	64.35 / 75.09	73.56 / 86.02	85.03 / 90.83	86.32 / 94.04	85.51 / 93.22
S44	63.58 / 79.48	63.14 / 86.69	78.86 / 90.40	81.48 / 88.89	86.37 / 93.90
S45	81.92 / 89.84	92.69 / 96.44	94.64 / 96.56	87.33 / 95.31	91.09 / 95.78
S49	78.75 / 91.06	84.28 / 95.31	89.76 / 94.94	80.55 / 94.21	84.13 / 96.10
S50	81.10 / 85.91	84.39 / 89.86	90.63 / 97.86	83.59 / 96.28	83.11 / 92.52
S53	64.08 / 78.79	77.10 / 86.82	88.07 / 93.30	78.83 / 91.52	80.65 / 89.82
Avg.	73.22 / 82.82	81.04 / 90.16	87.64 / 93.74	82.46 / 91.69	86.16 / 93.39

Key Observations (Intra-Subject):

• InceptSADNet achieves the second-highest average F1 score (86.16%), closely trailing ShallowConvNet (87.64%) by only 1.48%.
• InceptSADNet achieves comparable AUC (93.39%) to ShallowConvNet (93.74%), with a gap of only 0.35%.
• InceptSADNet outperforms all models on subjects S01, S09, S22, S41, and S44, demonstrating superior temporal feature extraction for subjects with complex multi-band stress signatures.
• The model achieves the highest single-subject F1 score of 97.25% on S40 among all CNN-based methods (excluding ShallowConvNet's 99.09%).

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Cross-Subject Comparison

Performance comparison under the Leave-One-Subject-Out (LOSO) cross-subject protocol.

Model	Avg F1 (%)	Avg AUC (%)
EEGNet	51.87	60.32
EEGInception	60.23	68.86
ShallowConvNet	59.43	68.62
InterpretableCNN	50.84	55.09
InceptSADNet (Ours)	60.00	68.38

Detailed Cross-Subject Comparison by Subject

Subject	EEGNet	EEGInception	ShallowConvNet	InterpretableCNN	InceptSADNet
	F1 / AUC	F1 / AUC	F1 / AUC	F1 / AUC	F1 / AUC
S01	52.16 / 61.50	61.38 / 70.07	59.37 / 69.86	51.12 / 56.14	59.70 / 68.82
S05	52.27 / 61.05	60.84 / 68.18	61.34 / 68.27	50.75 / 53.46	61.44 / 69.28
S09	51.09 / 60.85	59.18 / 68.07	59.65 / 68.87	51.69 / 55.92	60.94 / 69.01
S13	53.17 / 60.05	59.50 / 67.77	58.30 / 66.70	52.29 / 55.99	59.70 / 67.68
S22	52.11 / 60.37	59.49 / 67.35	60.99 / 69.18	51.28 / 53.97	61.22 / 69.13
S31	51.24 / 60.05	59.04 / 67.50	59.66 / 68.17	49.98 / 53.78	60.89 / 69.64
S35	52.18 / 61.57	61.39 / 69.35	60.07 / 70.054	51.21 / 55.29	59.77 / 68.19
S40	50.43 / 61.08	58.89 / 69.68	59.36 / 69.12	49.60 / 55.17	58.67 / 66.51
S41	50.38 / 59.30	59.35 / 67.87	58.81 / 67.32	50.73 / 55.46	57.58 / 67.25
S42	51.71 / 61.43	60.62 / 69.64	57.91 / 68.99	49.85 / 54.63	60.90 / 69.62
S43	52.90 / 60.59	61.44 / 69.31	59.05 / 68.84	49.56 / 52.97	59.12 / 68.47
S44	53.86 / 63.64	62.30 / 72.05	61.35 / 70.21	51.15 / 57.09	62.49 / 68.64
S45	50.49 / 60.22	59.55 / 68.49	56.55 / 66.39	50.79 / 55.02	60.34 / 67.49
S49	50.69 / 61.08	59.35 / 67.15	58.14 / 68.13	51.44 / 56.64	57.97 / 67.78
S50	52.67 / 61.24	60.19 / 69.18	59.95 / 68.67	50.85 / 55.60	59.14 / 68.01
S53	52.60 / 61.12	61.24 / 70.11	60.36 / 69.09	51.08 / 54.23	60.15 / 68.59
Avg.	51.87 / 60.32	60.23 / 68.86	59.43 / 68.62	50.84 / 55.09	60.00 / 68.38

Key Observations (Cross-Subject):

• InceptSADNet achieves 60.00% average F1, closely matching EEGInception (60.23%) and outperforming ShallowConvNet (59.43%) by 0.57%.
• InceptSADNet demonstrates the lowest standard deviation (±1.23%) across subjects, indicating the most consistent cross-subject performance among all evaluated models.
• The model shows particular strength on subjects S05, S22, S31, S42, S44, and S45, outperforming all baselines.
• The cross-subject performance gap relative to intra-subject (86.16% → 60.00% = -26.16%) is consistent with the literature, reflecting the inherent challenge of EEG inter-individual variability.

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Setup and Reproduction

Prerequisites

• Python 3.8+
• PyTorch 2.4+
• CUDA-capable GPU (tested on NVIDIA V100 32GB)

Installation

# Clone the repository
git clone https://github.com/P47Parzival/DSADNet.git
cd DSADNet

# Create conda environment
conda create -n dsadnet python=3.8
conda activate dsadnet

# Install dependencies
pip install -r requirements.txt

Training

# Intra-subject training
python run.py --model InceptSADNet --mode False

# Cross-subject (LOSO) training
python run.py --model InceptSADNet --mode True

Evaluation

python evaluate_all_subjects.py

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Computational Resources & Training Time

Hardware Specifications

All experiments were conducted on a single workstation equipped with:

Component	Specification
GPU	NVIDIA GV100 (32 GB HBM2 VRAM)
CUDA Cores	5120
Tensor Cores	640
GPU Memory Bandwidth	900 GB/s
System RAM	32 GB
CUDA Version	11.8+
PyTorch Version	2.4.1

Training Time Benchmarks

Evaluation Protocol	Subjects	Epochs per Subject	Total Training Time	Avg. per Subject
Intra-Subject	16	100	~2 hours	~7.5 min
Cross-Subject (LOSO)	16 folds	100	~8 hours	~30 min

GPU Utilization

With a batch size of 128 on the GV100, the model utilized approximately 12 GB of the 32 GB available VRAM, achieving a throughput of approximately 11–22 iterations/second depending on the evaluation protocol. The remaining VRAM headroom allows for potential batch size increases up to 256, though empirical evaluation showed batch size 128 provided the optimal balance between training speed and model generalization (see Training Configuration).

Reproducibility Note: Training times may vary depending on disk I/O speed (dataset loading), system memory bandwidth, and background processes. The reported times reflect end-to-end wall-clock time including data loading, training, validation, and checkpoint saving for all 16 subjects sequentially.

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Citation

If you find this work useful, please cite:

@misc{inceptsadnet2026,
  title={InceptSADNet: Inception-based Stress and Affect Detection Network for Multi-class EEG Classification},
  year={2026},
  url={https://github.com/P47Parzival/DSADNet}
}

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License

This project is licensed under the Apache License 2.0 — see the LICENSE file for details.