A New Paradigm for BCI Research
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A new paradigm for BCI research Conference or Workshop Item Other Submited paper on BCI research Daly, I., Nasuto, S. and Warwick, K. (2008) A new paradigm for BCI research. In: SSE Systems Engineering Conference 2008, 25-26 Sep 2008, The University of Reading. (Unpublished) Available at http://centaur.reading.ac.uk/1076/ It is advisable to refer to the publisher’s version if you intend to cite from the work. See Guidance on citing . All outputs in CentAUR are protected by Intellectual Property Rights law, including copyright law. Copyright and IPR is retained by the creators or other copyright holders. Terms and conditions for use of this material are defined in the End User Agreement . www.reading.ac.uk/centaur CentAUR Central Archive at the University of Reading Reading’s research outputs online A new paradigm for BCI research Ian Daly and Slawek J. Nasuto and Kevin Warwick1 Abstract. A new control paradigm for Brain Computer Interfaces the keyboard and the mouse. However an expanding field of cur- (BCIs) is proposed. rent research involves methods for directly interfacing between the BCIs provide a means of communication direct from the brain t a brain and a computer, bypassing motor movement control. Such di- computer that allows individuals with motor disabilities an additional rect interfacing opens up the possibility for additional channels of channel of communication and control of their external environment. communication and environmental control for individuals with mo- Traditional BCI control paradigms use motor imagery, frequency tor disabilities. rhythm modification or the Event Related Potential (ERP) as a means It should be noted at this stage that systems interfacing with the of extracting a control signal. brain can be classified as Brain Machine Interfaces (BMIs), where A new control paradigm for BCIs based on speech imagery is ini- BMIs usually refer to invasive interfacing and signal extraction tech- tially proposed. niques, and Brain Computer Interfaces (BCIs), which commonly re- Further to this a unique system for identifying correlations be- fer to noninvasive techniques [33]. However to avoid overly verbose tween components of the EEG and target events is proposed and in- language here we simply refer to all Brain Computer and Brain Ma- troduced. chine Interfaces as BCIs. BCI components can be broken down into five broad areas, these 1 Introduction are. Investigations into the possibility of creating a speech imagery based 1. Signal extraction. BCI are described. Such a BCI allows a degree of control of a com- 2. Signal processing techniques. puter system by the user without the need for muscle movement. 3. Feature extraction. Thus individuals with motor disabilities would be able to use the 4. Classification techniques. system to communicate and control their immediate environment. 5. Control paradigms. A speech imagery based BCI has several advantages over tradi- Signal extraction refers to the reading of levels of neurological tional BCI control paradigms. activity from individuals with the aim of extracting a useful control signal for our BCI. This can take two general forms, either invasive 1. It’s a more natural way for the user to communicate. Imagining techniques such as implanted electrode arrays or non-invasive tech- words is a much more direct means of communication then imag- niques such as the EEG. ing hand movements which is then used to control a cursor to Signal processing refers to the intermediate steps taken between ex- (among other things) select letters to spell a word. tracting a signal and classifying it. The techniques that can be used 2. No training is required on the part of the user. This reduces setup here are many and vary significantly depending on the type of signal time and increases user motivation. extraction method and specific hardware used. They can also depend 3. It’s a much more direct means of control for the user. Thus the user on the intended usage of our signal. However the general aim is al- will have a greater level of success and hence be more motivated ways to maximize the signal to noise ratio with respect to the features to use the system. of the signal that are the most interesting and usable for our purposes. Towards this end it becomes necessary to identify correlations Signal types within BCI encompass both the type of signal ex- within the EEG between speech imagery of specific words and cer- tracted and the way that signal is used. There are many different tain features of the EEG. Thus a unique solution to identifying these types of signals that can be extracted from the brain, these include correlations is outlined. EEG rhythms such as the mu rhythm (related to motor movement), Such a solution has numerous advantages to other researchers in neural firing spike trains (from invasive signal extraction methods), the BCI field. Hence our motivation is to create a unique solution for event related potentials and many more. identifying correlations between EEG features and specific tasks. Classification follows on from signal processing. Classification at- In researching such systems current methods within the BCI field tempts to determine whether a specific signal belongs to a specific are first identified. class group. That is; what command or stimulus presentation a spe- cific signal feature corresponds to. There are two paradigms for control of BCI systems; control based 1.1 Current BCIs - background paradigms and the goal based paradigms. Goal based BCIs present a selection of options to the user. The Traditional methods for interacting with computers are based on mo- user then chooses one of them as their target [33]. For example; in tor movement controlled interfaces. The most common of which are a BCI speller the subject will be presented with a selection of letters 1 University of Reading, UK, email: [email protected] from which they select the one they want. tween them. Damage to any region in this area would lead to aphasia (language disorders). From this theory naturally emerges the idea of functional local- ization within the brain. That is, we attribute specific localized areas specific functions, Broca’s area performs speech production, Wer- nicke’s area speech perception and together they are responsible for language. This idea was further strengthened by the formulation of a much more detailed model of the different specialized areas of lan- guage localization within the brain by Salomon Henschen in 1926 [15]. This model was based on previous clinical studies of patients with aphasia and the specific localization of lesions within their brains. The model has been elaborated on in further studies with even more specific sites added to the map of the brain developed by Brod- mann [20]. It’s important to note that there can be said to exist a level of local- ization of function within the brain. For example damage to Broca’s area will most likely result in loss of speech production abilities, al- though the extent of the loss of function and which specific speech functions are lost is still the subject of much research. It can how- ever be said with a very high level of confidence that when a subject speaks a high level of activity will occur within Broca’s area and when a subject listens to speech a high level of activity will occur Figure 1. Figure 1.0 - Typical EEG recording within Wernicke’s area. Furthermore it has been shown that this ac- tivity can be detected via EEG [6] using wavelet decomposition and independent component analysis to reveal high levels of EEG activity Control based BCIs by contrast allow the user to set their own within certain components at times corresponding to speech percep- goals [33]. For example; many motor imagery based BCIs such as tion. [7] make use of this paradigm. With these systems the user attempts Other research into EEG analysis of language has shown a level of to position a cursor or prosthetic device in any location. Typically the similarity between the mental rehearsal (imagination) of a language cursor or prosthetic’s velocity along one or more directional axis is function and it’s implementation [5]. This is akin to the level of sim- controlled by the strength of some neurological signal component. ilarity exhibited between the mental imagery of a motor function and We can thus say that goal based BCIs are discrete, digital systems it’s implementation [19]. It is upon this principle that the majority of and control based BCIs are open, analog systems. the current BCI research into motor control is based. Additionally fMRI is shown to exhibit specific activity patterns during linguistics tasks such as word perception and production. 1.2 Neurolinguistics Analysis of EEG using fuzzy logic to classify wavelet decompo- Of key importance for attempts to develop a speech production based sitions of the signal is shown to correctly classify speech imagery BCI is the current research into neurolinguistics. That is, research tasks fr three simple nouns (colour names; red, green and blue). This into the neurological mechanisms of speech perception and produc- demonstrates that it is possible to use a range of imaging techniques tion. Therefore the following introduces relevant neurolinguistic con- to identify neurological processes related to speech perception and cepts. production. Further to this it is possible to correctly identify the per- The key areas in the brain involved in speech are Broca’s area ception and imagined production of different words in the EEG. which lies on the third convolution in the left brain hemisphere and It is therefore reasonable to hypothesize that a BCI based upon the was first identified by Paul Broca in 1861 [29]. This region has been imagination of speech production is a feasible area of research.