Key result
A wireless multi-channel EEG recording system and an integrated classification method using NLEO, AR model, and bisecting k-means algorithm were developed to acquire and cluster EEG signals.
The development of a wireless EEG system with an integrated clustering algorithm allows for long-term EEG monitoring without cables and faster data classification.
A multi-channel wireless EEG (electroencephalogram) acquisition and recording system is developed in this work. The system includes an EEG sensing and transmission unit and a digital processing circuit. The former is composed of pre-amplifiers, filters, and gain amplifiers. The kernel of the later digital processing circuit is a micro-controller unit (MCU, TI-MSP430), which is utilized to convert the EEG signals into digital signals and fulfill the digital filtering. By means of Bluetooth communication module, the digitized signals are sent to the back-end such as PC or PDA. Thus, the patient's EEG signal can be observed and stored without any long cables such that the analogue distortion caused by long distance transmission can be reduced significantly. Furthermore, an integrated classification method, consisting of non-linear energy operator (NLEO), autoregressive (AR) model, and bisecting k-means algorithm, is also proposed to perform EEG off-line clustering at the back-end. First, the NLEO algorithm is utilized to divide the EEG signals into many small signal segments according to the features of the amplitude and frequency of EEG signals. The AR model is then applied to extract two characteristic values, i.e., frequency and amplitude (peak to peak value), of each segment and to form characteristic matrix for each segment of EEG signal. Finally, the improved modified k-means algorithm is utilized to assort similar EEG segments into better data classification, which allows accessing the long-term EEG signals more quickly.
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Lin et al. (2006) studied EEG signal acquisition and processing. Wireless multi-channel EEG recording system and integrated classification method was evaluated on System development and EEG off-line clustering. A wireless multi-channel EEG recording system and an integrated classification method using NLEO, AR model, and bisecting k-means algorithm were developed to acquire and cluster EEG signals.
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