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B. Blankertz, G. Dornhege, M. Krauledat, K.-R. Müller, V. Kunzmann, F. Losch and G. Curio, “The Berlin Brain-Computer Interface: EEG-Based Communication Without Subject Training,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 14, 2006, pp. 147- 152. http://dx.doi.org/10.1109/TNSRE.2006.875557

has been cited by the following article:

• TITLE: Mean Threshold and ARNN Algorithms for Identification of Eye Commands in an EEG-Controlled Wheelchair

  AUTHORS: Nguyen Thanh Hai, Nguyen Van Trung, Vo Van Toi

  KEYWORDS: Autoregressive NN Model; Threshold algorithm; EEG Technology; Eye Activity and Electrical Wheelchair

  JOURNAL NAME: Engineering, Vol.5 No.10B, October 31, 2013

  ABSTRACT: This paper represented Autoregressive Neural Network (ARNN) and meant threshold methods for recognizing eye movements for control of an electrical wheelchair using EEG technology. The eye movements such as eyes open, eyes blinks, glancing left and glancing right related to a few areas of human brain were investigated. A Hamming low pass filter was applied to remove noise and artifacts of the eye signals and to extract the frequency range of the measured signals. An autoregressive model was employed to produce coefficients containing features of the EEG eye signals. The coefficients obtained were inserted the input layer of a neural network model to classify the eye activities. In addition, a mean threshold algorithm was employed for classifying eye movements. Two methods were compared to find the better one for applying in the wheelchair control to follow users to reach the desired direction. Experimental results of controlling the wheelchair in the indoor environment illustrated the effectiveness of the proposed approaches.