Showing papers by "Kai Keng Ang published in 2008"

Filter Bank Common Spatial Pattern (FBCSP) in Brain-Computer Interface

Abstract: In motor imagery-based brain computer interfaces (BCI), discriminative patterns can be extracted from the electroencephalogram (EEG) using the common spatial pattern (CSP) algorithm. However, the performance of this spatial filter depends on the operational frequency band of the EEG. Thus, setting a broad frequency range, or manually selecting a subject-specific frequency range, are commonly used with the CSP algorithm. To address this problem, this paper proposes a novel filter bank common spatial pattern (FBCSP) to perform autonomous selection of key temporal-spatial discriminative EEG characteristics. After the EEG measurements have been bandpass-filtered into multiple frequency bands, CSP features are extracted from each of these bands. A feature selection algorithm is then used to automatically select discriminative pairs of frequency bands and corresponding CSP features. A classification algorithm is subsequently used to classify the CSP features. A study is conducted to assess the performance of a selection of feature selection and classification algorithms for use with the FBCSP. Extensive experimental results are presented on a publicly available dataset as well as data collected from healthy subjects and unilaterally paralyzed stroke patients. The results show that FBCSP, using a particular combination feature selection and classification algorithm, yields relatively higher cross-validation accuracies compared to prevailing approaches.

A clinical evaluation of non-invasive motor imagery-based brain-computer interface in stroke

Abstract: This clinical study investigates whether the performance of hemiparetic stroke patients operating a non-invasive Motor Imagery-based Brain-Computer Interface (MI-BCI) is comparable to healthy subjects. The study is performed on 8 healthy subjects and 35 BCI-naive hemiparetic stroke patients. This study also investigates whether the performance of the stroke patients in operating MI-BCI correlates with the extent of neurological disability. The performance is objectively computed from the 10×10-fold cross-validation accuracy of employing the Filter Bank Common Spatial Pattern (FBCSP) algorithm on their EEG measurements. The neurological disability is subjectively estimated using the Fugl-Meyer Assessment (FMA) of the upper extremity. The results show that the performance of BCI-naive hemiparetic stroke patients is comparable to healthy subjects, and no correlation is found between the accuracy of their performance and their motor impairment in terms of FMA.

A clinical evaluation on the spatial patterns of non-invasive motor imagery-based brain-computer interface in stroke

Abstract: This clinical study investigates whether the spatial patterns of hemiparetic stroke patients operating a non-invasive Motor Imagery-based Brain Computer Interface (MI-BCI) is comparable to healthy subjects. The spatial patterns for a specific frequency range are generated using the common spatial pattern (CSP) algorithm, of which is highly successful for discriminating two classes of EEG measurements in MI-BCI. The spatial patterns illustrate how the presumed sources project on the scalp and are effective in verifying the neurophysiological plausibility of the computed solution. The spatial patterns show focused activity in ipsilateral as well as contralateral hemisphere with respect to the hand by tapping or motor imagery in 2 BCI-artful healthy subjects and 12 BCI-naive hemiparetic stroke patients. The results also show that neurophysiologically interpretable spatial patterns is more common in performing motor imagery compared to finger tapping by hemiparetic stroke patients. Hence, this shows that hemiparetic stroke patients are capable of operating MI-BCI.

Multiclass voluntary facial expression classification based on Filter Bank Common Spatial Pattern

Abstract: This paper investigates the classification of voluntary facial expressions from electroencephalogram (EEG) and electromyogram (EMG) signals using the Filter Bank Common Spatial Pattern (FBCSP) algorithm. The FBCSP algorithm is an autonomous and effective machine learning approach for classifying two classes of EEG measurements in motor imagery-based Brain Computer Interface (BCI). However, the problem of facial expression recognition typically involves more than just two classes of measurements. Hence, this paper proposes an extension of FBCSP to the multiclass paradigm using a decision threshold-based classifier for classifying facial expressions from EEG and EMG measurements. A study is conducted using the proposed Multiclass FBCSP on 4 subjects who performed 6 different facial expressions. The results show that the Multiclass FBCSP is effective in classifying multiple facial expressions from the EEG and EMG measurements.

Augmenting cognitive processes in robot-assisted motor rehabilitation

Abstract: Cognitive processes, such as motor intention, attention, and higher level motivational states are important factors that govern motor performance and learning. Current robot-assisted rehabilitative programs focus only on the physical aspects of training. In this paper, we propose a framework for motor rehabilitation based on the augmentation of cognitive channels of patient-robot interactions and using it to deliver a more optimal therapy. By examining the cognitive processes involved in motor control and adaptation, it is argued that optimal therapy needs to be considered in the context of a complete motor scheme consisting not only of sensorimotor signals, but also their interactions with cognitive operations, such as motor planning, attention, and motivation, which mediate motor learning. We outline a few BCI-based modules for the detection and monitoring of relevant cognitive processes, which provide inputs for the robot to automatically modulate parameters of the rehabilitation protocol. Preliminary investigations on a BCI module for detection of motor intention, performed on a small group of stroke patients, show feasible accuracies.