Browsing by Author "Chang, Meng-Hsiu"

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FPGA Implementation of 4-Channel ICA for On-line EEG Signal Separation
(2008)
Huang, Wei-Chung
;
Hung, Shao-Hang
;
Chung, Jen-Feng
;
Chang, Meng-Hsiu
;
Van, Lan-Da
Blind source separation of independent sources from their mixtures is a common problem for multi-sensor applications in real world, for example, speech or biomedical signal processing. This paper presents an independent component analysis (ICA) method with information maximization (Infomax) update applied into 4-channel one-line EEG signal separation. This can be implemented on FPGA with a fixed-point number representation, and then the separated signals are transmitted via Bluetooth. As experimental results, the proposed design is faster 56 times than soft performance, and the correlation coefficients at least 80% with the absolute value are compared with off-line processing results. Finally, live demonstration is shown in the DE2 FPGA board, and the design is consisted of 16,605 logic elements.
Review of Wireless and Wearable Electroencephalogram Systems and Brain-Computer Interfaces - A Mini-Review
(2010)
Lin, Chin-Teng
;
Ko, Li-Wei
;
Chang, Meng-Hsiu
;
Duann, Jeng-Ren
;
Chen, Jing-Ying
Biomedical signal monitoring systems have rapidly advanced in recent years, propelled by significant advances in electronic and information technologies. Brain-computer interface (BCI) is one of the important research branches and has become a hot topic in the study of neural engineering, rehabilitation, and brain science. Traditionally, most BCI systems use bulky, wired laboratory-oriented sensing equipments to measure brain activity under well-controlled conditions within a confined space. Using bulky sensing equipments not only is uncomfortable and inconvenient for users, but also impedes their ability to perform routine tasks in daily operational environments. Furthermore, owing to large data volumes, signal processing of BCI systems is often performed off-line using high-end personal computers, hindering the applications of BCI in real-world environments. To be practical for routine use by unconstrained, freely-moving users, BCI systems must be noninvasive, nonintrusive, light-weight and capable of online signal processing. This work reviews recent online BCI systems, focusing especially on wearable, wireless and real-time systems. Copyright (C) 2009 S. Karger AG, Basel