Fast Multiclass SVM Classification Using Decision Tree Based One-Against-All Method

Decision tree structure based classification of EEG signals recorded during two dimensional cursor movement imagery

BACKGROUND

Input signals of an EEG based brain computer interface (BCI) system are naturally non-stationary, have poor signal to noise ratio, depend on physical or mental tasks and are contaminated with various artifacts such as external electromagnetic waves, electromyogram and electrooculogram. All these disadvantages have motivated researchers to substantially improve speed and accuracy of all components of the communication system between brain and a BCI output device.

NEW METHOD

In this study, a fast and accurate decision tree structure based classification method was proposed for classifying EEG data to up/down/right/left computer cursor movement imagery EEG data. The data sets were acquired from three healthy human subjects in age group of between 24 and 29 years old in two sessions on different days.

RESULTS

The proposed decision tree structure based method was successfully applied to the present data sets and achieved 55.92%, 57.90% and 82.24% classification accuracy rate on the test data of three subjects.

COMPARISON WITH EXISTING METHOD(S)

The results indicated that the proposed method provided 12.25% improvement over the best results of the most closely related studies although the EEG signals were collected on two different sessions with about 1 week interval.

CONCLUSIONS

The proposed method required only a training set of the subject and automatically generated specific DTS for each new subject by determining the most appropriate feature set and classifier for each node. Additionally, with further developments of feature extraction and/or classification algorithms, any existing node can be easily replaced with new one without breaking the whole DTS. This attribute makes the proposed method flexible.