Decoding EEG Brain Signals using Recurrent Neural Networks

Inhaltsverzeichnis (Table of Contents)

• Problem description
• Tasks
• Bibliography
• Abstract

Zielsetzung und Themenschwerpunkte (Objectives and Key Themes)

The main objective of this thesis is to develop a recurrent neural network algorithm to decode EEG brain signals during four motor imagery movements (left, right, both hands and rest). The algorithm will be trained offline on CPU or GPU using Theano packages, with the optional goal of converting it to a Spiking neural network for the highly power-efficient TrueNorth Neuromorphic hardware. This research aims to improve the accuracy and efficiency of brain-machine interfaces (BMI) for rehabilitation robotics.

• Decoding EEG signals for motor imagery movements
• Developing recurrent neural network algorithms for EEG signal analysis
• Improving accuracy and efficiency of BMI systems
• Exploring the potential of Spiking neural networks for low-power EEG decoding
• Utilizing deep learning models like LSTM and CNN for EEG signal classification

Zusammenfassung der Kapitel (Chapter Summaries)

• Problem description: This section introduces the concept of Motor Imagery Electroencephalography (MI-EEG) and its significance in brain-machine interfaces (BMI) for rehabilitation robotics. It highlights the challenges of decoding non-stationary and noisy EEG signals and the potential of deep learning algorithms to address these challenges.
• Tasks: This section outlines the key tasks involved in the thesis project. It includes a review of EEG decoding literature, development and training of a recurrent neural network for EEG decoding, comparison of results with existing methods, and optional implementation on TrueNorth IBM's chip.
• Abstract: This section provides a concise overview of the thesis, focusing on the use of deep learning models like LSTM and CNN for decoding motor imagery movements from EEG signals. It mentions the development of both models, the use of data augmentation techniques, and the evaluation on a novel EEG dataset.

Schlüsselwörter (Keywords)

The primary keywords and focus topics of this thesis include: EEG decoding, recurrent neural networks, LSTM, CNN, motor imagery, brain-machine interfaces, rehabilitation robotics, deep learning, data augmentation, TrueNorth Neuromorphic hardware, Spiking neural networks.