Deep Learning-Driven EEG Classification in Human-Robot Collaboration

Abstract: Human-robot collaboration (HRC) occurs when people and robots work together in a shared environment. Current robots often use rigid programs unsuitable for HRC. Multimodal robot programming offers an easier way to control robots using inputs like voice and gestures. In this scenario, human commands from different sensors trigger the robot’s actions. However, this data-driven approach has challenges: accurately understanding power dynamics, integrating inputs, and precisely controlling the robot. To address this, we introduce EEG signals to improve robot control, requiring reliable signal processing, feature extraction, and accurate classification using machine learning and deep learning. Existing deep learning models struggle to balance accuracy and efficiency. This thesis focuses on whether dilated convolutional neural networks can improve accuracy and reduce training and reaction times compared to the baseline. After using the Morlet wavelet for EEG feature extraction, in the thesis, an existing convolutional neural network as a benchmark is employed and uses the dilated convolution algorithm for comparison. Accuracy, precision, recall, and time are used to assess the comparison algorithm’s performance. The conclusion is that the dilated convolutional neural network performs better than the baseline in accuracy and time parameters.