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Automatic classification between active brain state vs. rest state in healthy subjects and stroke patients

Mocioiu, V. (2012) Automatic classification between active brain state vs. rest state in healthy subjects and stroke patients.

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Abstract:Several methods exist for stroke rehabilitation. One method is the practice of motor imagery. The effect of this approach is improved by neurofeedback. This is done by using electroencephalographic (EEG) signals in a brain computer interface (BCI) setup. The BCI system should give the patient neurofeedback according to his sensorimotor rhythm. Our goal was to find a way to model the two states associated with the sensorimotor rhythm: synchronized (rest) and desynchronized (active). For this purpose we have investigated four band power features: broad-band (8 - 30 Hz), α-band (8 - 13 Hz), β-band (13 - 30 Hz), and userdefined band and two classification methods: linear discriminant analysis (LDA) and support vector machines (SVM). Furthermore, we have employed a spatial filtering method, namely common spatial patterns (CSP), to see if classification outcomes could be improved. Since the eventual aim is to build a system that can be used at home, we examined several electrode configurations in order to find out the minimum number of electrodes needed to control the system. We extracted the features for different periods (8, 6, 4, and 2 seconds) to see what the influence on all of the above parameters was. Results show that the highest performances were obtained on average for the broad-band feature, but the other features display good performances as well. We found that the highest classifier performances were obtained for the combination of CSP and SVM, with the general remark that SVM outperforms LDA. The minimum number of electrodes that was needed to ensure reliable control of the system was two. The investigated trial lengths seem not to influence all of the above parameters, good performances being found for all of them. We consider that CSP is not suited for stroke data because it tends to focus on irrelevant aspects of the data. We deliberate that five channels is the minimum number of channels that can be used in an online system. We have also argued that the results are not influenced by trial length because the features are weakly stationary.
Item Type:Essay (Master)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:53 electrotechnology
Programme:Electrical Engineering MSc (60353)
Link to this item:http://purl.utwente.nl/essays/69777
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