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

Figure 3 Overall architecture of the proposed lightweight FusedNeXt model for electroencephalography-based binary classification of anxiety. The network processes 224 × 224 × 3 electroencephalography topographic maps through a stem block (4 × 4 convolution, 96 filters, stride 4; equation 4), followed by four progressive FusedNeXt stages with channel widths of 96, 192, 384, and 768. Each stage applies two sequential hyper-connected blocks (block A → block B) twice in succession: Block A (spatial-mixing; equations 5-7) performs spatial mixing using a 3 × 3 depthwise convolution branch in parallel with a 1 × 1 grouped convolution branch, fused by additive hyper-connection; block B (channel-mixing; equations 8-10) implements a transformer-style inverted bottleneck (4 × channel expansion followed by 1 × 1 projection) alongside a complementary 3 × 3 grouped-convolution branch, again fused by additive hyper-connection. Three transition blocks (2 × 2 grouped convolution with stride 2; equation 12) perform learnable spatial downsampling between stages. The classification head (equations 14-17) consists of batch normalization, global average pooling, a 2-unit fully connected layer, and a softmax activation, producing the final anxiety/control probability distribution. Total complexity: ≈ 7.17 M parameters, ≈ 1.95 GFLOPs. EEG: Electroencephalography; GELU: Gaussian Error Linear Unit; FC: Fully connected layer.