Detecting driver's braking intention using recurrent convolutional neural networks based EEG Analysis

Keyphrases

• Neural Network 100%
• Braking Intention 100%
• Electroencephalography Analysis 100%
• Recurrent Convolutional Neural Network 100%
• Convolutional Neural Network Model 71%
• Event-related Potentials 57%
• Early Events 57%
• Emergency Situation 42%
• Brake Pedal 28%
• Potential Curves 28%
• AUC Score 28%
• Electroencephalography 14%
• Classification Performance 14%
• User Intention 14%
• Brain-computer Interface Systems 14%
• Effective Method 14%
• Post-stimulus 14%
• Driving Situation 14%
• Normal Driving 14%
• Emergency Braking 14%
• Brain Signals 14%
• Performance-based 14%
• Detection Time 14%
• High Temporal Resolution 14%
• Internal Data 14%
• Curve-based 14%
• Virtual Driving 14%
• Layer Two 14%
• Potential Pattern 14%
• External Information 14%
• Driving Environment 14%
• Device Information 14%
• Regularized Linear Discriminant Analysis 14%
• Driving Intention 14%
• Convolutional Layer 14%
• Visual Sensory 14%
• Softmax Layer 14%
• Stepping On 14%
• Sensory Processes 14%
• Recurrent Layer 14%
• Driving Assistance System 14%

Engineering

• Recurrent 100%
• Convolutional Neural Network 100%
• Network Model 62%
• Emergency Situation 37%
• Convolutional Layer 25%
• Brain-Computer Interface 12%
• Internals 12%
• Classification Performance 12%
• Brain Signal 12%
• Temporal Resolution 12%

Computer Science

• Convolutional Neural Network 100%
• Neural Network Model 71%
• Emergency Situation 42%
• Convolutional Layer 28%
• Computer Interface 14%
• Classification Performance 14%
• Spatial Pattern 14%
• Interface System 14%
• Effective Method 14%
• Linear Discriminant Analysis 14%
• Sensory Process 14%
• Internal Information 14%
• Temporal Resolution 14%