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.

As silicon carbide(SiC) gets more and more attention from the semiconductor industry due to its robust mechanical and chemical properties, reliable and standardized processing technologies such as reactive ion etching(RIE) for SiC are in great demand. This is because of the difficulty and challenge of fabricating micro devices on the SiC substrate. Although the high hardness and chemical inertness make SiC a good candidate for applications such as sensors in harsh environments, they also impede the development of SiC-based devices when considering processing. This thesis aims to develop a standardized inductively coupled plasma(ICP) reactive ion etching(RIE) process for 4H-SiC substrate etching. The developed process is expected to be applied in the fabrication of micro-electro-mechanical systems(MEMS). The specifications are a high etch rate, micro-masking-free surface, and high selectivity. First, a literature review was conducted to comprehensively study the characteristics of the SiC material and the mechanisms of the ICP RIE process. Second, a baseline recipe was developed guided by the theory studied in the literature works. Third, initial tests were conducted, and the preliminary optimizations with a focus on etch rate and micro masking suppression were performed. Fourth, the design of experiments(DOE) based on the preliminarily optimized recipe was conducted to study the effect of process parameters on etch rate, etch profile, and selectivity. Last, the optimized recipes with a focus on etch rate, etch profile, and selectivity were summed and listed. The achieved maximum etch rate was 1.26 µm/min. The maximum selectivity of the hard mask material to SiC was 153 when the nickel hard mask was used. Amicro-masking-free surface of SiC was achieved.