Brain-Computer Interface

Abstract

Brain-computer interface (BCI) provides a communication channel for patients with sever neuromuscular disorders to signal their intentions directly with their brain activities, instead of the normal output pathways between the brain and muscles. When a subject is performing specific tasks, the electroencephalographic (EEG) signals induced by her/his neuronal activities are recorded, analyzed, and translated to the corresponding commands for computers or other devices. A BCI system can be synchronous or asynchronous depending on whether the task is cue-triggered or self-paced. Asynchronous BCI systems are more practical yet more complicated due to the requirement of continuous analysis of ongoing EEG without any timing information about the mental status of the subject.

Results

Toward building an asynchronous BCI system, we develop signal preprocessing and classification techniques, including signal preprocessing, feature extraction, feature selection, and classification, that can be used to continuously discriminate between EEG recordings when the subject is resting or performing left-hand/right-hand imagery tasks. The acquired EEG signals are first filtered for artifact removal. Then we use Morlet wavelet (Figure 1) to extract time-frequency components.

 

Figure : The averaging Morlet wavelet coefficients of one of our dataset. The x-axis and y-axis represents respectively 'time' and 'frequency'. The color represent the coefficient values. We analyze the data from 4s to 8s and the frequency range is between 8-30 Hz. The three rows represent wavelet coefficients over C3, Cz and C4 positions and the left and right column represent the left hand imagery task and the right hand imagery task. As we can see in the figure, after performing Morlet wavelet transformation, we can easily find the contralateral desynchronization at mu band 3.75s ~ 4.25s.

 

 

During the training stage, these abundant components are examined through t statistic and forward feature selection and the components with large discernment capability can be determined. During the classification stage, the EEG signals go through the same preprocessing procedure and the discriminative wavelet components are calculated. Then, two one-class classifiers are applied to discriminate the resting state from the left-hand motor imagery and from the right-hand motor imagery, respectively. The one-class classifier focuses only on the feature distribution of one of the motor imagery task on which the subject concentrates. In this way, we do not need to model the widespreading distribution of the resting state, which may comprise slight but fickle mental task.