| Summary: | 博士 === 國立交通大學 === 電控工程研究所 === 105 === Significant advances in neuroscience, sensor technologies, and efficient signal processing algorithms have greatly facilitated the transition from laboratory-oriented neuroscience research to practical applications. Brain-computer interfaces (BCIs) represent major strides in translating brain signals into actionable decisions and primarily consist of front-end signal processing and back-end signal analyzing that guide the communications between users and systems. This dissertation presents several current neuro network technologies and computational intelligence methods applied to EEG-based BCIs. In the front-end signal processing aspect, novel portable EEG devices featuring dry electrodes are introduced as substitutes for traditional BCIs with wet electrodes and its bulky size. Meanwhile, in the back-end signal analyzing aspect, fuzzy neural networks and information fusion techniques are introduced to address the technical issues of complex brain network description, and decision fusion, respectively. For instance, information fusion technique has been utilized to attack the individual differences problem of motor imagery applications in the real-world environment. This dissertation also presents BCIs system with a novel classification method that uses electrooculography (EOG) signals, which provide another communication way between humans and machine. With continuous improvements in the development of a convenient approach to record brain signals and extract knowledge regarding intentions, BCI techniques are envisioned to lead to a wide range of real-life applications in the near future.
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