![]() ![]() ![]() The proposed system is tested on two separate datasets. A set of prepossessing, feature extraction and classification methods were implemented to help support and validate the proposed system and to benchmark it against the latest developed systems in BCI literature. This thesis develops an asynchronous multiclass noninvasive EEG-based BCI system which is based on a novel feature extraction method for asynchronous BCI. However, it is a challenging problem and requires the development of appropriate machine learning and signal processing tools. The asynchronous multiclass BCI problem is particular importance because it closely matches realistic operating conditions (as opposed to synchronous problems). Although Noninvasive Electroencephalogram-based (EEG-based) BCI is showing a lot of promise, it is faced with a number of difficult challenges, especially from the perspective of signal processing, because the signals being observed by EEG are extremely weak and typically contain very high levels of noise. It can be used to allow paralyzed as well as healthy individuals to interact with and control the surrounding environment or to communicate simply by the conscious modulation of thought patterns. A Brain Computer Interface (BCI) is a system which allows direct communication between the brain and a computer.
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