A brain-computer interface (BCI) for the locked-in: comparison of different EEG classifications for the thought translation device.
ABSTRACT The Thought Translation Device (TTD) for brain-computer interaction was developed to enable totally paralyzed patients to communicate. Patients learn to regulate slow cortical potentials (SCPs) voluntarily with feedback training to select letters. This study reports the comparison of different methods of electroencephalographic (EEG) analysis to improve spelling accuracy with the TTD on a data set of 6,650 trials of a severely paralyzed patient.
Selections of letters occurred by exceeding a certain SCP amplitude threshold. To enhance the patient's control of an additional event-related cortical potential, a filter with two filter characteristics ('mixed filter') was developed and applied on-line. To improve performance off-line the criterion for threshold-related decisions was varied. Different types of discriminant analysis were applied to the EEG data set as well as on wavelet transformed EEG data.
The mixed filter condition increased the patients' performance on-line compared to the SCP filter alone. A threshold, based on the ratio between required selections and rejections, resulted in a further improvement off-line. Discriminant analysis of both time-series SCP data and wavelet transformed data increased the patient's correct response rate off-line.
It is possible to communicate with event-related potentials using the mixed filter feedback method. As wavelet transformed data cannot be fed back on-line before the end of a trial, they are applicable only if immediate feedback is not necessary for a brain-computer interface (BCI). For future BCIs, wavelet transformed data should serve for BCIs without immediate feedback. A stepwise wavelet transformation would even allow immediate feedback.
Article: Brain computer interfaces, a review.[show abstract] [hide abstract]
ABSTRACT: A brain-computer interface (BCI) is a hardware and software communications system that permits cerebral activity alone to control computers or external devices. The immediate goal of BCI research is to provide communications capabilities to severely disabled people who are totally paralyzed or 'locked in' by neurological neuromuscular disorders, such as amyotrophic lateral sclerosis, brain stem stroke, or spinal cord injury. Here, we review the state-of-the-art of BCIs, looking at the different steps that form a standard BCI: signal acquisition, preprocessing or signal enhancement, feature extraction, classification and the control interface. We discuss their advantages, drawbacks, and latest advances, and we survey the numerous technologies reported in the scientific literature to design each step of a BCI. First, the review examines the neuroimaging modalities used in the signal acquisition step, each of which monitors a different functional brain activity such as electrical, magnetic or metabolic activity. Second, the review discusses different electrophysiological control signals that determine user intentions, which can be detected in brain activity. Third, the review includes some techniques used in the signal enhancement step to deal with the artifacts in the control signals and improve the performance. Fourth, the review studies some mathematic algorithms used in the feature extraction and classification steps which translate the information in the control signals into commands that operate a computer or other device. Finally, the review provides an overview of various BCI applications that control a range of devices.Sensors 01/2012; 12(2):1211-79. · 1.74 Impact Factor
Advances in Human-Computer Interaction. 01/2011;
Conference Proceeding: Usability of Brain Computer Interfaces[show abstract] [hide abstract]
ABSTRACT: Abstract. A Brain Computer Interface (BCI) is a control and/or communication system in which the user’s commands and messages do not depend on muscular control. If BCIs were to be considered and evaluated as assistive technology facilitating daily activities, they could avoid dissatisfaction and prevent abandonment. Approaches such as “User-centered design”, “User interfaces for all” and the most recent “Integrated model of usability” already highlight the importance of a complete and full evaluation of the interaction. The tradition of Human Computer Interaction (HCI) has already given us most of the tools we need to analyse and evaluate technology. In a series of studies, we assessed the usability of two BCI prototypes by measuring interaction with the systems in context, considering the performance, cognitive workload and satisfaction of non-disabled users in order to better understand how the interface affects these parameters. We tested two keyboard-controlled Java BCI prototypes based on the Thought Translation Device and the P300 Speller (P3S). In the first evaluation, we tested the learnability of BCIs on 6 healthy users through the Thinking Aloud technique, which showed that while all users easily learned how the system worked with Language Support Program (LSP), they failed with P3S. We then tested BCI efficiency on 30 participants (15 with LSP and 15 with P3S) through the Copy Spelling Task (CST) and administered the System Usability Scale (SUS) to measure usability and the Survey of Technology Use (SOTU) scale of the Matching Person and Technology (MPT) to measure predisposition to the use of technology. With the CST, we found that P3S users were more accurate in selecting and recognising letters on the screen. Both SUS and SOTU did not show any significant effects. After modifying our testing paradigm, we tested again using 61 participants with different computer skills, and administered usability and cognitive workload questionnaires. The results showed significant differences in the number of performed errors as well as in user satisfaction and the cognitive workload invested in the task. We found that the Thought Translation Device was more error-resistant, less stressful and more satisfactory for the users compared to the P3S.AAATE 2011 - Association for the Advancement of Assistive Technology in Europe, Maastricht, the Netherlands; 09/2011