D2.1 Requirements and Specification - CORBYS

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D2.1 Requirements and Specification These signals are associated to motor task preparation, and dissimilar to those during the actual execution, in Figure 43 EEG activities related to preparation and execution are shown. The intention to move is related with at least three cortical activities, specifically, the readiness potential (or Bereitschaftspotential -BP), the contingent negative variation (CNV) and the event-related (de)synchronisation (ERD/ERS). Figure 43: Voluntary hand movement phases: motion intention, motion execution and motion end The readiness potential starts approximately 2.0 s before the movement onset and, as pointed out in the first reports (Kornhuber & Deecke, 1964, 1965) and in subsequent studies (Deecke et al, 1969, 1976; Kutas & Donchin, 1980), rapidly increases its gradient about 400ms before the muscular activity. It is characterised by a maximal activity in the midline centro-parietal area and by a symmetrical and wide scalp distribution independently of the site of movement. The BP occurrence, in respect to the movement onset, differs among subjects and movement conditions. The early and the late segment of the BP are called “early BP” and “late BP“ respectively due to their different scalp distribution. The late BP, for its asymmetric distribution associated with unilateral hand movement, has been studied as the lateralised readiness potential (LRP) (Coles et al, 1988). The contingent negative variation, a similar slow negativity preceding movements, occurs during the interval between a warning stimulus (S1) and a subsequent imperative stimulus (S2) before the movement onset (Walter et al, 1974; Tecce, 1972). The earlier part of the CNV can be distinguished from the later one (or terminal CNV -tCNV) based on the different scalp distribution. The former is generated in response to S1 and it is maximal over the frontal cortex; the latter can start up to 1.5 s before S2 and it is characterised by a maximal activity over the frontal and prefrontal cortices indicating cognitive preparation, and over the primary motor cortex (M1) and supplementary motor area (SMA) reflecting motor preparation (Rohrbaugh et al, 1976; Rosahl & Knight, 1995; Ikeda et al, 1996). The event-related desynchronisation/synchronisation (ERD/ERS) reflect the power changes of EEG oscillatory activity of various frequency bands, they are related to various tasks including voluntary movement. In the case of motor task preparation alpha (8-13 Hz) and beta (14-30 Hz) are the frequency bands to analysed (Shibasaki & Hallett, 2006). The ERD, corresponding to a power decrease, is related to increased neural activity of the associated cortical area, while the ERS, corresponding to a power increase, is associated with decreased activation (Pfurtscheller et al, 1997). The ERD starts about 1.5 s before the movement onset and it is localised over the contralateral precentral and postcentral area of the scalp. At approximately 750- 158
D2.1 Requirements and Specification 500 ms before the muscular activity desyncronization with relevant magnitude can be measured over the ipsilateral scalp as well. Instead, ERS starts about 1 s prior to the movement over the occipital cortex (Bastiaansen et al, 1999). Several research labs have analysed these EEG signals. Most of them have been focusing on upper extremities pre-movement signals, mainly in the discrimination between right and left hand. Just few of them have been investigating the lower extremities (e.g. legs). So far there are no studies in discriminating between right and left leg. The following list provides an insight into a representative variety of works related to premotor potentials that are currently being pursued in research groups: � Morash and his group utilised CNV and ERD/ERS to predict which of four movements (right-hand squeeze, left hand squeeze, tongue press on the roof of the mouth, and right foot toe curl) was about to occur (Morash et al, 2008). EEG signals were recorded from eight, right-handed, healthy, BCI naive subjects using 29 channels over sensorimotor areas. The movement was instructed with a specfic stimulus (S1) and performed at a second stimulus (S2). A spatial filter (ICA) and a temporal filter (DWT) were used in the pre-processing while an off-line evaluation was done using a naive Bayesian (BSC) classifier. An average classification accuracy of 40% was reached. The results of this study suggest that the ERD/ERSD is the most specific neural signal preceding movements, related to the particular movement. � Pfurtscheller and his group analysed similar intention of movements (left index finger, right index finger and right foot) to show the feasibility of automatic methods for the selection of optimal electrode position and optimal number of channels for an EEG-based brain state classifier (Peters et al, 2001). Data collected from three healthy subjects recorded with 56 electrodes, developed and presented in a previous study (Pfurtscheller et al, 1994), were used. Spatial and frequency filtering were applied, especially for the former several techniques were compared showing common average reference (CAR) and Laplace filter as the best. Classification was performed using an artificial neural network (ANN) with three perceptrons for each channel, where its output is an ”opinion“ in the majority voting process for the automatic selection. High classification accuracy was obtained for left/right index finger discrimination (93%) and for left/right index finger and right foot discrimination (89%). Anyway the data were collected from subjects who were prompted to specific tasks by a computer. � The Berlin Brain Computer Interface Group has given an important contribution with various studies. Blankertz utilised the LRP to predict single-trial EEG potentials preceding voluntary finger movement (Blankertz et al, 2003). Eight healthy subjects were instructed to press keyboard keys in a self-chosen order and timing. The signal was first filtered using a Fourier transform filtering technique and after classified with a linear classifier (LDA). They managed to predict the laterality of imminent hand finger movements (right vs. left), and demonstrate the possibility to achieve good accuracy even at fast motor command rates (2 taps per second) with a single-trial EEG paradigm. The latter result is very relevant, since it takes into account fast motor sequences condition, and start to analyse how after-effects of one movement superimpose on the preparation of a consecutive movement (offline). In a similar experimental setting Blankertz compared different kind of classifiers, Support Vector Machines (SVMs) and variants of Fisher Discriminant, reaching high accuracy level (>96%). Beyond the previous classifier a second one was trained, to distinguish movements events from the rest (Blankertz et al, 2002). Krauledat showed the possibility of using the LRP in motor task classification even in time critical context (Krauledat et al, 2004). The EEG was recorded with 27 up to 120 electrodes while the subject had to respond as quickly as possible with finger movements to different 159
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