[show abstract][hide abstract] ABSTRACT: There has been an increase in research interest for brain-computer interface (BCI) technology as an alternate mode of communication and environmental control for the disabled, such as patients suffering from amyotrophic lateral sclerosis (ALS), brainstem stroke and spinal cord injury. Disabled patients with appropriate physical care and cognitive ability to communicate with their social environment continue to live with a reasonable quality of life over extended periods of time. Near-infrared spectroscopy is a non-invasive technique which utilizes light in the near-infrared range (700 to 1000 nm) to determine cerebral oxygenation, blood flow and metabolic status of localized regions of the brain. In this paper, we describe a study conducted to test the feasibility of using multichannel NIRS in the development of a BCI. We used a continuous wave 20-channel NIRS system over the motor cortex of 5 healthy volunteers to measure oxygenated and deoxygenated hemoglobin changes during left-hand and right-hand motor imagery. We present results of signal analysis indicating that there exist distinct patterns of hemodynamic responses which could be utilized in a pattern classifier towards developing a BCI. We applied two different pattern recognition algorithms separately, Support Vector Machines (SVM) and Hidden Markov Model (HMM), to classify the data offline. SVM classified left-hand imagery from right-hand imagery with an average accuracy of 73% for all volunteers, while HMM performed better with an average accuracy of 89%. Our results indicate potential application of NIRS in the development of BCIs. We also discuss here future extension of our system to develop a word speller application based on a cursor control paradigm incorporating online pattern classification of single-trial NIRS data.
[show abstract][hide abstract] ABSTRACT: We report the implementation of a text input application (speller) based on the P300 event related potential. We obtain high accuracies by using an SVM classifier and a novel feature. These techniques enable us to maintain fast performance without sacrificing the accuracy, thus making the speller usable in an online mode. In order to further improve the usability, we perform various studies on the data with a view to minimizing the training time required. We present data collected from nine healthy subjects, along with the high accuracies (of the order of 95% or more) measured online. We show that the training time can be further reduced by a factor of two from its current value of about 20 min. High accuracy, fast learning, and online performance make this P300 speller a potential communication tool for severely disabled individuals, who have lost all other means of communication and are otherwise cut off from the world, provided their disability does not interfere with the performance of the speller.
IEEE Transactions on Neural Systems and Rehabilitation Engineering 04/2006; 14(1):24-9. · 3.26 Impact Factor
[show abstract][hide abstract] ABSTRACT: We report our studies on a Brain Computer Interface (BCI) speller application with an aim to optimize its performance and usability. We study the dependence of the spelling accuracy as a function of (a) the number of visual stimuli (repetitions) presented to the user, (b) the P300 segment length used, (c) the number of channels used, and (d) the amount of data used in training, in terms of the number of characters and repetitions. Reducing the number of repetitions results in a direct reduction of the time needed to spell a character, while minimizing the number of channels translates to shorter subject preparation time and thus improves the usability of the system. The usability is further enhanced by decreasing the training required, while maintaining the accuarcy. We show that very high accuracies of the order of 99% can be achieved with a short training session of less than 10 minutes using only about 10 channels. The high accuracies, short training and preparation time requirements along with real-time performance make this BCI speller a viable communication tool for severely disabled individuals, who have no other means to communicate with the external world.
Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 02/2005; 5:5396-9.
[show abstract][hide abstract] ABSTRACT: P300 speller is a communication tool with which one can input texts or commands to a computer by thought. The amplitude of the P300 evoked potential is inversely proportional to the probability of infrequent or task-related stimulus. In existing P300 spellers, rows and columns of a matrix are intensified successively and randomly, resulting in a stimulus frequency of 1/N (N is the number of rows or columns of the matrix). We propose a new paradigm to display each single character randomly and individually (therefore reducing the stimulus frequency to 1/(N*N)). On-line experiments showed that this new speller significantly improved the performance. Specifically, the new speller can reduce character classification error rate by up to 80% or double the information transfer rate compared to the existing P300 spellers.
Biomedical Circuits and Systems, 2004 IEEE International Workshop on; 01/2005
[show abstract][hide abstract] ABSTRACT: Improving classification accuracy is a key issue to advancing brain computer interface (BCI) research from laboratory to real world applications. This work presents a high accuracy EEG signal classification method using single trial EEC signal to detect left and right finger movement. We apply an optimal temporal filter to remove irrelevant signal and subsequently extract key features from spatial patterns of EEG signal to perform classification. Specifically, the proposed method transforms the original EEG signal into a spatial pattern and applies the RBF feature selection method to generate robust feature. Classification is performed by the SVM and our experimental result shows that the classification accuracy of the proposed method reaches 90% as compared to the current reported best accuracy of 84%.
Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on; 09/2004
[show abstract][hide abstract] ABSTRACT: We report the effect of removing ocular artifacts on the performance of a word-processing application based on the event related potential P300. Various methods of removing artifacts have been reported. The efficiency of these algorithms are usually done by subjective visual comparisons. Noting that there is a direct correlation of artifact rectifying algorithms to the accuracy in a brain computer interface system's accuracy, we present this work as a means to compare different algorithms.
Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 02/2004; 6:4385-8.