Journal of Medical and Biological Engineering, 24(1): 9-15 9 Comparison of Adaptive Cancellation and Laplacian Operation in Removing Eye Blinking Artifacts in EEG Chou-Ching K. Lin Shun-Lung Chaung 1 Ming-Shaung Ju *,1 Yung-Nien Sun 2 Department of Neurology, University Hospital, National Cheng Kung University, Tainan, 701, Taiwan R.O.C. 1 Department of Mechanical Engineering, National Cheng Kung University, Tainan, 701, Taiwan R.O.C. 2 Department of Computer Science and Information Engineering, Tainan, 701, Taiwan R.O.C. Received 5 January 2004; Accepted 24 February 2004 Abstract The long-term goal of our project is to develop a brain computer interface for locked-in patients. In this study, we designed a controlled experiment and compared the efficacy of real-time adaptive cancellation and Laplacian operation in removing eye blinking artifacts in EEG. Scalp EEG was recorded while the subject performed thumb movements in three different states of eye blinking, i.e., persisted eye opening, persisted eye closure and natural blinking. The collected data were preprocessed with one of three preprocessing algorithms, namely, adaptive cancellation, Laplacian operation and null and, then, passed through windowed Fourier transform to calculate the change of wave power. Templates of wave power were derived by averaging the whole set. Correlation coefficients of templates and single-pass experimental results were calculated and a threshold value of coefficient was chosen to define the detection of thumb movements. The validity of detection was tested by EMG of thumb extensor. The efficacy of preprocessing algorithms was evaluated by ANOVA and chi-square tests. The results showed that, compared with the control group, both adaptive cancellation and Laplacian operation enhanced the wave suppression percentage. There is no difference between the group results of two preprocessing methods, while the individual difference is prominent. The implication of the effect of preprocessing on enhancing event detection rate is discussed. Keywords: EEG, Adaptive cancellation, Eye blinking, Laplacian operation Introduction Electrical activities (e lectroe ncephalog ram, EEG), which reflect the state of brain activations, can be recorded on the scalp by electrodes. µ rhythm is the 8-12 Hz waveform recorded at the Rolandic area of cortex (C3 and C4 of the standard international 10-20 electrode system) while the subject is wakeful and relaxed. Since µ rhythm is suppressed by the voluntary movements and sensory stimuli of contralateral upper limbs [1], it is a potential candidate for the control source in b rain-c omputer-i nterface (BCI) technology [1-5]. Eye movements are common source of artifacts in EEG recording [7]. Though eye movements may not change the topographical asymmetry of alpha and beta wave, they exert substantial general effects on the whole EEG spectrum [8]. It is not well studied how significant eye movements can affect µ waves. Different eye movements have different topographies and have to be treated individually [9]. Most of the past studies about µ rhythm discarded the experimental data interfered *Corresponding author: Ming-Shaung Ju Tel: +886-6-2757575 ext.62163; Fax: +886-6-2352973 E-mail: msju@mail.ncku.edu.tw by the eye blinking by naked eye inspection. Yet, if EEG is to be used as a real-time control source in BCI, the interference due to eye blinking has to be eliminated by signal processing procedures in real time. For removing artifacts due to eye movements, many techniques have been developed in the past, ranging from simple thresholding [10] and linear regression [11-13] to more sophisticated methods, such as aligned-artifact average [14], independent component analysis [15], discrete cosine transform [16] and adaptive linear processing [17]. Simple methods were unsatisfactory in performance while more sophisticated methods have better performance at the cost of more extensive calculation. For BCI applications, the algorithm has to be effective and, at the same time, simple in order to be implemented in real time. The main purpose of this study was to develop an algorithm to process EEG for identifying the attempts of thumb movements in real time under the natural eye blinking condition. The algorithm consisted of two parts. The first preprocessing part was to remove the influence of eye blinking. The second part, the template correlation, was to identify the movement attempt. In contrast to most of the previous studies, we considered not only the true positive results, but also false positive and negative cases.