This paper presents a simple self-paced motor imagery based brain-computer interface (BCI) to control a robotic wheelchair. An innovative control protocol is proposed to enable a 2-class self-paced BCI for wheelchair control, in which the user makes path planning and fully controls the wheelchair except for the automatic obstacle avoidance based on a laser range finder when necessary. In order for the users to train their motor imagery control online safely and easily, simulated robot navigation in a specially designed environment was developed. This allowed the users to practice motor imagery control with the core self-paced BCI system in a simulated scenario before controlling the wheelchair. The self-paced BCI can then be applied to control a real robotic wheelchair using a protocol similar to that controlling the simulated robot. Our emphasis is on allowing more potential users to use the BCI controlled wheelchair with minimal training; a simple 2-class self paced system is adequate with the novel control protocol, resulting in a better transition from offline training to online control. Experimental results have demonstrated the usefulness of the online practice under the simulated scenario, and the effectiveness of the proposed self-paced BCI for robotic wheelchair control.
Due to the non-stationarity of EEG signals, online training and adaptation are essential to EEG based brain-computer interface (BCI) systems. Self-paced BCIs offer more natural human-machine interaction than synchronous BCIs, but it is a great challenge to train and adapt a self-paced BCI online because the user's control intention and timing are usually unknown. This paper proposes a novel motor imagery based self-paced BCI paradigm for controlling a simulated robot in a specifically designed environment which is able to provide user's control intention and timing during online experiments, so that online training and adaptation of the motor imagery based self-paced BCI can be effectively investigated. We demonstrate the usefulness of the proposed paradigm with an extended Kalman filter based method to adapt the BCI classifier parameters, with experimental results of online self-paced BCI training with four subjects.
A novel 4-class single-trial brain computer interface (BCI) based
on two (rather than four or more) binary linear discriminant analysis
(LDA) classifiers is proposed, which is called a “parallel BCI.” Unlike
other BCIs where mental tasks are executed and classified in a serial
way one after another, the parallel BCI uses properly designed parallel
mental tasks that are executed on both sides of the subject body
simultaneously, which is the main novelty of the BCI paradigm used
in our experiments. Each of the two binary classifiers only classifies
the mental tasks executed on one side of the subject body, and the
results of the two binary classifiers are combined to give the result
of the 4-class BCI. Data was recorded in experiments with both real
movement and motor imagery in 3 able-bodied subjects. Artifacts
were not detected or removed. Offline analysis has shown that, in
some subjects, the parallel BCI can generate a higher accuracy than a
conventional 4-class BCI, although both of them have used the same
feature selection and classification algorithms.
We present our design and online experiments of a 3-class asynchronous BCI controlling a simulated robot in an indoor environment. Two characteristics of our design have efficiently decreased the false positive rate during the NC (No Control) mode. First, three one-vs-rest LDA classifiers are combined to control the switching between NC and IC (In Control) mode. Second, the hierarchical structure of our controller allows the most reliable class (mental task) in a specific subject to play a dominant role in the robot control. A group of simple rules triggered by local sensor signals are designed for safety and obstacle avoidance in the NC mode. In online experiments, subjects successfully controlled the robot to circumnavigate obstacles and reach small targets in separate rooms.
Aiming at developing asynchronous BCIs, we tested 21 2-class combinations of 7 mental tasks to determine whether any pair of tasks may be more suitable. The tasks under consideration were: auditory recall, mental navigation, sensorimotor attention (left hand), sensorimotor attention (right hand), mental calculation, imaginary movement (left hand), imaginary movement (right hand). Sensorimotor attention is novel in this application domain. All possible pairs were tried in 5 subjects using data from 10s periods in which subjects were free to execute the required mental task at their own pace. Recordings were done whilst the subject controlled a robot navigation simulator on a computer monitor, with the robot direction being related to the mental task. Classification of the data was done using LDA. Class-separation was estimated using the Davies-Bouldin index. Best classification results were obtained when auditory recall was followed or preceded by mental calculation. Of the possible 21 task combinations, this task pair was in the top 5 (performance-wise) for 4 of the 5 subjects. This was also the case when class-separation was used as a criterion.
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