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      A Survey of Stimulation Methods Used in SSVEP-Based BCIs

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Brain-computer interface (BCI) systems based on the steady-state visual evoked potential (SSVEP) provide higher information throughput and require shorter training than BCI systems using other brain signals. To elicit an SSVEP, a repetitive visual stimulus (RVS) has to be presented to the user. The RVS can be rendered on a computer screen by alternating graphical patterns, or with external light sources able to emit modulated light. The properties of an RVS (e.g., frequency, color) depend on the rendering device and influence the SSVEP characteristics. This affects the BCI information throughput and the levels of user safety and comfort. Literature on SSVEP-based BCIs does not generally provide reasons for the selection of the used rendering devices or RVS properties. In this paper, we review the literature on SSVEP-based BCIs and comprehensively report on the different RVS choices in terms of rendering devices, properties, and their potential influence on BCI performance, user safety and comfort.

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          Human EEG responses to 1-100 Hz flicker: resonance phenomena in visual cortex and their potential correlation to cognitive phenomena.

          C S Herrmann (2001)
          The individual properties of visual objects, like form or color, are represented in different areas in our visual cortex. In order to perceive one coherent object, its features have to be bound together. This was found to be achieved in cat and monkey brains by temporal correlation of the firing rates of neurons which code the same object. This firing rate is predominantly observed in the gamma frequency range (approx. 30-80 Hz, mainly around 40 Hz). In addition, it has been shown in humans that stimuli which flicker at gamma frequencies are processed faster by our brains than when they flicker at different frequencies. These effects could be due to neural oscillators, which preferably oscillate at certain frequencies, so-called resonance frequencies. It is also known that neurons in visual cortex respond to flickering stimuli at the frequency of the flickering light. If neural oscillators exist with resonance frequencies, they should respond more strongly to stimulation with their resonance frequency. We performed an experiment, where ten human subjects were presented flickering light at frequencies from 1 to 100 Hz in 1-Hz steps. The event-related potentials exhibited steady-state oscillations at all frequencies up to at least 90 Hz. Interestingly, the steady-state potentials exhibited clear resonance phenomena around 10, 20, 40 and 80 Hz. This could be a potential neural basis for gamma oscillations in binding experiments. The pattern of results resembles that of multiunit activity and local field potentials in cat visual cortex.
          Article 0 comments Cited 242 times – based on 0 reviews     Review now
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            An online multi-channel SSVEP-based brain-computer interface using a canonical correlation analysis method.

            Bo Hong, Guangyu Bin, Xiaorong Gao … (2009)
            In recent years, there has been increasing interest in using steady-state visual evoked potential (SSVEP) in brain-computer interface (BCI) systems. However, several aspects of current SSVEP-based BCI systems need improvement, specifically in relation to speed, user variation and ease of use. With these improvements in mind, this paper presents an online multi-channel SSVEP-based BCI system using a canonical correlation analysis (CCA) method for extraction of frequency information associated with the SSVEP. The key parameters, channel location, window length and the number of harmonics, are investigated using offline data, and the result used to guide the design of the online system. An SSVEP-based BCI system with six targets, which use nine channel locations in the occipital and parietal lobes, a window length of 2 s and the first harmonic, is used for online testing on 12 subjects. The results show that the proposed BCI system has a high performance, achieving an average accuracy of 95.3% and an information transfer rate of 58 +/- 9.6 bit min(-1). The positive characteristics of the proposed system are that channel selection and parameter optimization are not required, the possible use of harmonic frequencies, low user variation and easy setup.
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              A survey of signal processing algorithms in brain-computer interfaces based on electrical brain signals.

              Rabab Ward, Mehrdad Fatourechi, Ali Bashashati … (2007)
              Brain-computer interfaces (BCIs) aim at providing a non-muscular channel for sending commands to the external world using the electroencephalographic activity or other electrophysiological measures of the brain function. An essential factor in the successful operation of BCI systems is the methods used to process the brain signals. In the BCI literature, however, there is no comprehensive review of the signal processing techniques used. This work presents the first such comprehensive survey of all BCI designs using electrical signal recordings published prior to January 2006. Detailed results from this survey are presented and discussed. The following key research questions are addressed: (1) what are the key signal processing components of a BCI, (2) what signal processing algorithms have been used in BCIs and (3) which signal processing techniques have received more attention?
              Article 0 comments Cited 136 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Journal ID (nlm-ta): Comput Intell Neurosci
                Journal ID (publisher-id): CIN
                Title: Computational Intelligence and Neuroscience
                Publisher: Hindawi Publishing Corporation
                ISSN (Print): 1687-5265
                ISSN (Electronic): 1687-5273
                Publication date (Print): 2010
                Publication date (Electronic): 7 March 2010
                Publication date PMC-release: 7 March 2010
                Volume: 2010
                Electronic Location Identifier: 702357
                Affiliations
                1Department of Signal Processing Systems, Technical University Eindhoven, 5600 MB Eindhoven, The Netherlands
                2Department of Brain, Body & Behavior, Philips Research Eindhoven, 5656 AE Eindhoven, The Netherlands
                3College of Biomedical Engineering and Instrument Science, Zhejiang University, 310027, China
                4Department of Artificial Intelligence, Radboud University Nijmegen, 6500 HE Nijmegen, The Netherlands
                Author notes

                Academic Editor: Francois Vialatte

                Article
                DOI: 10.1155/2010/702357
                PMC ID: 2833411
                PubMed ID: 20224799
                SO-VID: 4a079422-c606-4c68-9b3b-8055bd176db9
                Copyright © Copyright © 2010 Danhua Zhu et al.
                License:

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 24 September 2009
                Date accepted : 4 January 2010
                Categories
                Subject: Review Article

                ScienceOpen disciplines: Neurosciences
                Data availability:
                ScienceOpen disciplines: Neurosciences

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