Aspectos legales para la Protección del Secreto de las Telecomunicaciones

Mental strategy

It is well known that MI activates similar brain regions as physical motor actions. Several studies have shown that MI has therapeutic effect in motor rehabilitation [83, 84, 85]. But the claimed therapeutic effect of MI has been challenged [233]. As discussed earlier in Section 2.3.1, one refrain from performing an action if one is truly only imagining the action. For motor rehabilitation it would be better if a patient practises the motor action as close as possible to normal practice. Refraining from performing the action would not correspond to normal physical practice. In addition, patient might have reduced imagery ability [230, 234, 235]. Therefore it would be better if patients instead use AM. With AM, patients can more easily mimic the actual action. They can also easily understand the paradigm than when asked to imagine abstractly.

Signal source

Usually a BCI classifier is computed for a patient using BCI for rehabilitation in a similar manner as healthy individuals. In the case of the patients, the signal used to compute the classifier may have been affected by the injury. For example, due to reorganisations in the brain and other factors the EEG corresponding to the left hand movement attempt may differ from that prior to SCI. Using such a ‘malformed’ signal for rehabilitation of the hand may not give the best results. The aim of rehabilitation of the left hand will be to bring the functioning of the hand as close as possible to what it was prior to the injury. In order to achieve the best results, it is theoretically advisable to use a signal obtained during the normal workings of the hand which can be obtained prior to the injury. Since this optimal signal is often not available the next option will be to approximate it. The approximated signal is hopefully better than a malformed version. The issue then is how to approximate the signal. A starting point is to use signals recorded from healthy individuals. Such signals averaged from a large number of individuals provide data representing the signal in humans. Since the averaged signal may not be specific to an individual, experienced persons can add modifications after studying a patients own ‘malformed’ signal. Using ERD phenomenon in EEG during hand movement as an example, it may not be necessary to modify the averaged signal given that the individual differences occur majorly at different frequency bands which usually overlap and are within only two distinct physiological bands namely the α and the β-bands defined within 7-30 Hz. So a patient’s optimal EEG signal for rehabilitation would not be vastly different from that obtained from a cohort of healthy individuals.

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EEG feature

One of the most important aspect that should be considered when choosing parameters is the setup time. When a long setup time is required in rehabilitation application, patient might be left tired or even asleep before the setup is completed. In addition, hospitalised patients may only have limited allowed time for each therapy session and it will be unhelpful to spend most of the available time setting up the system. Long setup times can be caused mainly by (1) the use of large number of electrodes (e.g >16) and (2) the need to record large amount of classifier training dataset. The second will be discussed later. The first can be avoided by choosing EEG features that do not require large number of electrodes for classification. Example of such features includes the TDP or bandpower methods and the CSP method based on about sixteen channels. An advantage of the CSP method is that it offers good classification accuracy. The TDP and the bandpower methods however allow the use of fewer electrodes placed in desired physiological locations and the bandpower method has been used in BCI-FES more than the other features as can be seen from Table 2.2. The TDP method was used in this thesis, instead of the bandpower method, where it was shown to be a good feature for a therapeutic BCI. It mainly deviate from the classical band- power method because it does not require narrow band filtered signal. A possible drawback of the classical bandpower method is this requirement to select subject specific narrow fre- quency bands. This is unlike in the CSP methods where a classical frequency band of 8-30 Hz incorporating the so called α (8-12 Hz) and β (13-30 Hz) bands is used. Using a single wider frequency band in the range of 8-30 Hz for bandpower will contribute in three main ways. (1) Power calculation can be more accurately performed in shorter time windows. (2) The use of a single frequency band leads to a reduced feature dimensionality which should favour a classifier like LDA that works by reducing the dimension of the features. (3) A more general classifier can be obtained since all relevant frequency band is included. The TDP method used in this thesis gives these three benefits. Despite using wide band filtered signal, narrow bandpower changes in the α and β bands can still be targeted [236] with TDP. This is because during AM for example, bandpower changes should only occur dominantly in narrow α and β bands so the other frequencies would be redundant in the decision of a classifier. A large band of 8-30 Hz can even perform better than narrow frequency bands [129, 237].

Traditional BCI has relied on features that are directly dependent on EEG power. In the bid to move BCI research forward, there is the need for future investigations to look at other methods. For example, using connectivity between EEG sources [238] or the connectiv- ity between EEG channels [239] should be further investigated. Such methods might offer improvements in BCI [239].

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Feature classifier

The LDA classifier has been shown to be a good classifier in BCI. From Table 2.2, it has been often used in BCI-FES. One advantage of the LDA classifier is that it is relatively straight forward to update online as shown in Section 3.1.2. The classifier weight and bias can be updated online depending on the deviation of the values of the feature matrix [130]. Updating the classifier online has two main advantages. (1) A previous classifier can be reused by updating it online and changes in the feature characteristic during a BCI session can be accommodated. (2) There is no need to record large amount of training dataset since a small training dataset can be used to compute an initial classifier which can be updated online. This reduces setup time significantly.

In order to achieve the uttermost classification accuracy, classification should be performed on one AM against the resting/relaxed state. Most researchers as shown in Table 2.2 have chosen this scheme. This means that during a rehabilitation session, the restoration of one movement is targeted at a time. The benefit is that the reduced number of false positives that may arise when classifying between two active tasks may result in better rehabilitation outcome.

Performance feedback

Since feedback plays an important role in BCI and in rehabilitation, the provision of timely and accurate feedback is necessary. From the past BCI-FES studies shown in Table 2.2, the authors have used visual, audio, tongue stimulation and tactile feedbacks. Some authors use a combination of visual and audio feedbacks.

The feedbacks should be continuous rather than binary based since a continuous feedback is more natural like the normal continuous visual input to the brain. Possibly the best feedback would constitute a visual feedback in which the patient watches his/her own body physically moved (by FES) continuously to complete the movement the patient initiated. This would feel more realistic and could better encourage rehabilitation.

It is useful for the patient user to be given a score of their ability to control the BCI system. Such a score can motivate the patients and help in their active participation. The score can also give a meaningful measure of a patient’s progress on the use of the BCI system. In order to compute a meaningful score across sessions, there might be a need to obtain a method of comparing different BCI sessions. This is because due to variations in settings and setup in different sessions, a user might have a different control level at each session. This can be dealt with by setting a difficulty level and keeping it the same in each session. A method of setting the difficulty level is described in Section 3.1.4.

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