With the growing developments in Artificial Intelligence (AI), deep learning models have become an attractive solution for industrial applications such as machine health monitoring and predictive maintenance. To enable real-time analysis and reduce reliance on cloud infrastructure, it is often more practical to process sensor data directly on edge devices. However, while deep learning models offer improved performance, their high memory and computational demands often exceed the limited resources of edge devices. Moreover, compression requires a lot of hyperparameter tuning, which is unique for each model, layer, and application. To address these limitations, this work utilizes dynamic Bayesian compression, which reduces model size and computational costs. By introducing learnable gate variables that control the quantization precision and the rank of decomposed factors, the model can adaptively determine the most efficient configuration for each layer during training. This results in a more flexible, end-to-end trainable compression scheme that maintains performance while significantly improving deployability on edge devices.

In the context of designing a real-time brain-computer interface for playing a game using the OpenBCI Ultracortex "Mark IV" headset, this paper focuses on the work of the decoding subgroup. The primary responsibility is to analyse EEG data retrieved from the OpenBCI headset and classify the intention of the user. Our objective is to achieve a high-accuracy classification of the EEG signals. The paper is structured into three main sections: preprocessing, feature extraction, and classification. Multiple methods for preprocessing and classification of motor execution EEG signals will be analysed, striving to contribute to the real-time implementation of the project. The results of our work provides valuable insights for future research and development in this field.