Drowsiness detection using EEG signals using DL about dl-simplified HOT 6 CLOSED

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What are the CNN architectures you are planning to use here?
Apart from CNN what are the models you are planning to implement for this EEG dataset?

@KamakshiOjha

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Input Layer:

• Input Shape: (30, 128, 1)

Convolutional Layers:

• Branch 1:

  • Conv2D with 16 filters, kernel size (1, 3), ReLU activation
  • Conv2D with 32 filters, kernel size (1, 3), ReLU activation
  • Conv2D with 64 filters, kernel size (1, 3), ReLU activation
  • cbam_block applied to the output

• Branch 2:

  • Conv2D with 16 filters, kernel size (1, 5), ReLU activation
  • Conv2D with 32 filters, kernel size (1, 5), ReLU activation
  • Conv2D with 64 filters, kernel size (1, 5), ReLU activation
  • cbam_block applied to the output

• Branch 3:

  • Conv2D with 16 filters, kernel size (1, 7), ReLU activation
  • Conv2D with 32 filters, kernel size (1, 7), ReLU activation
  • Conv2D with 64 filters, kernel size (1, 7), ReLU activation
  • cbam_block applied to the output

Combined Branch:

• The outputs from the branches are combined using an Add layer.
• Further Convolutional Layers:
  • Conv2D with 64 filters, kernel size (1, 3), ReLU activation
  • Conv2D with 128 filters, kernel size (1, 3), ReLU activation
  • Conv2D with 128 filters, kernel size (1, 3), ReLU activation

Global Pooling and Fully Connected Layers:

• GlobalAveragePooling2D applied to the last convolutional layer.
• Fully connected (Dense) layers:
  • Dense with 512 units, ELU activation
  • Dense with 256 units, ELU activation
  • Dense with 128 units, ELU activation
  • Dense with 32 units, ELU activation
  • Output Dense with 2 units, Softmax activation (for classification)

from dl-simplified.

Since my dataset consists of EEG signals, I've experimented with various CNN architectures to find the best results. Additionally, I've incorporated an attention mechanism into my model to enhance its performance.

from dl-simplified.

Input Layer:

• Input Shape: (30, 128, 1)

Convolutional Layers:

• Branch 1:

  • Conv2D with 16 filters, kernel size (1, 3), ReLU activation
  • Conv2D with 32 filters, kernel size (1, 3), ReLU activation
  • Conv2D with 64 filters, kernel size (1, 3), ReLU activation
  • cbam_block applied to the output

• Branch 2:

  • Conv2D with 16 filters, kernel size (1, 5), ReLU activation
  • Conv2D with 32 filters, kernel size (1, 5), ReLU activation
  • Conv2D with 64 filters, kernel size (1, 5), ReLU activation
  • cbam_block applied to the output

• Branch 3:

  • Conv2D with 16 filters, kernel size (1, 7), ReLU activation
  • Conv2D with 32 filters, kernel size (1, 7), ReLU activation
  • Conv2D with 64 filters, kernel size (1, 7), ReLU activation
  • cbam_block applied to the output

Combined Branch:

• The outputs from the branches are combined using an Add layer.

• Further Convolutional Layers:

  • Conv2D with 64 filters, kernel size (1, 3), ReLU activation
  • Conv2D with 128 filters, kernel size (1, 3), ReLU activation
  • Conv2D with 128 filters, kernel size (1, 3), ReLU activation

Global Pooling and Fully Connected Layers:

• GlobalAveragePooling2D applied to the last convolutional layer.

• Fully connected (Dense) layers:

  • Dense with 512 units, ELU activation
  • Dense with 256 units, ELU activation
  • Dense with 128 units, ELU activation
  • Dense with 32 units, ELU activation
  • Output Dense with 2 units, Softmax activation (for classification)

Cool. Go ahead with this approach.

Assigned @KamakshiOjha

from dl-simplified.

Hello @KamakshiOjha! Your issue #784 has been closed. Thank you for your contribution!

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