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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(More)
This paper presents a novel hands-free control system for an electric-powered wheelchair, which is based on EMG (Electromyography) signals recorded from eyebrow muscle activity. By using a simple CyberLink device, one-dimensional continuous EMG signals are obtained, analysed, and then translated into multi-directional control commands (forward, left, right,(More)
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(More)
Due to the non-stationarity of EEG signals, online training and adaptation is essential to EEG based brain-computer interface (BCI) systems. Asynchronous BCI offers more natural human-machine interaction, but it is a great challenge to train and adapt an asynchronous BCI online because the user's control intention and timing are usually unknown. This paper(More)
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(More)
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)(More)
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(More)
Ultrasonic imaging based on the pulse-echo principle is widely used throughout the world, particularly in medical applications. However, its spatial resolution is poor (around 2 times the wavelength, or 200 μm at 15 MHz), limiting its ability to detect small but clinically important lesions (such as microcalcifications in breast cancer). The work presented(More)