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function layers = EEGNetModel(in_chans, n_classes, varargin)
% EEGNetv4 creation function for MATLAB
% Default parameters
p = inputParser;
addRequired(p, 'in_chans');
addRequired(p, 'n_classes');
addRequired(p, 'input_window_samples');
addParameter(p, 'pool_mode', 'mean');
addParameter(p, 'F1', 8);
addParameter(p, 'D', 2);
addParameter(p, 'F2', 16);
addParameter(p, 'kernel_length', 64);
addParameter(p, 'third_kernel_size', [8, 4]);
addParameter(p, 'drop_prob', 0.25);
parse(p, in_chans, n_classes, varargin{:});
% Extract parameters from parsed input
params = p.Results;
% EEGNetv4 Layers
% First set of layers
layers = [
imageInputLayer([params.in_chans, params.input_window_samples, 1], 'Normalization', 'none')
convolution2dLayer([1, params.kernel_length], params.F1, 'Stride', [1, 1], 'Padding',[0, floor(params.kernel_length / 2)])
batchNormalizationLayer()
convolution2dLayer([params.in_chans, 1], params.F1*params.D, 'Stride', [1, 1], 'Padding', [0, 0])
batchNormalizationLayer()
reluLayer()
averagePooling2dLayer([1, 4], 'Stride', [1, 4])
dropoutLayer(params.drop_prob)
];
% Second set of layers (Depthwise Separable Convolution)
layers = [
layers
convolution2dLayer([1, 16], params.F1*params.D, 'Stride', [1, 1], 'Padding', [0, 8])
convolution2dLayer([1, 1], params.F2, 'Stride', [1, 1], 'Padding', [0, 0])
batchNormalizationLayer()
reluLayer()
averagePooling2dLayer([1, 8], 'Stride', [1, 8])
dropoutLayer(params.drop_prob)
];
% Third set of layers
layers = [
layers
convolution2dLayer([1, 23], params.n_classes)
softmaxLayer()
classificationLayer()
];
% Convert layers to layerGraph
% lgraph = layerGraph(layers);
% Convert layerGraph to dlnetwork
% net = dlnetwork(lgraph);
end
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