Keun Tae Kim

Commanding a Brain-Controlled Wheelchair Using Steady-State Somatosensory Evoked Potentials

In this work, we propose a novel brain-controlled wheelchair, one of the major applications of brain–machine interfaces (BMIs), that allows an individual with mobility impairments to perform daily living activities independently. Specifically, we propose to use a steady-state somatosensory evoked potential (SSSEP) paradigm, which elicits brain responses to tactile stimulation of specific frequencies, for a user’s intention to control a wheelchair. In our system, a user had three possible commands by concentrating on one of three vibration stimuli, which were attached to the left-hand, right-hand, and right-foot, to selectively control the wheelchair. The three stimuli were associated with three wheelchair commands: turn-left, turn-right, and move-forward. From a machine learning perspective, we also devise a novel feature representation by combining spatial and spectral characteristics of brain signals. In order to validate the effectiveness of the proposed SSSEP-based system, we considered two different tasks: 1) a simple obstacle-avoidance task within a limited time and; 2) a driving task along the predefined trajectory of about 40 m length, where there were a narrow pathway, a door, and obstacles. In both experiments, we recruited 12 subjects and compared the average time of motor imagery (MI) and SSSEP-based controls to complete the task. With the SSSEP-based control, all subjects successfully completed the task without making any collision while four subjects failed it with MI-based control. It is also noteworthy that in terms of the average time to complete the task, the SSSEP-based control outperformed the MI-based control. In the other more challenging task, all subjects successfully reached the target location.

Design of a robotic wheelchair with a motor imagery based brain-computer interface

This paper presents a prototype for an electro-encephalogram (EEG) based brain-actuated wheelchair system using motor imagery. To overcome some of the limitations of other previous works, such as gaze dependence and unnecessary stops, five commands (left, left-diagonal, right, right-diagonal, and forward) were decoded based on the motor imagery correlates in EEG signals. Also, the system was modularized into three components: BCI control, and network. On the basis of the conclusions, we can expect a robust brain-actuated wheelchair system, which can allow the users intention to control the wheelchair in multi-directional movements, thereby increasing the users authority compared with many of the alternative approaches.

Towards an EEG-based intelligent wheelchair driving system with vibro-tactile stimuli

Nowadays, the electroencephalography (EEG)-based wheelchair driving system, one of the major applications of brain-computer interface (BCI), that allows an individual with mobility impairments to perform daily living activities independently. In this context, users intention identifying methods were developed by several research groups using various paradigms for the wheelchair driving. In this study, we use a steady-state somatosensory evoked potential (SSSEP) paradigm, which elicits brain responses to vibro-tactile stimulation of specific frequencies, for a users intention identification to driving a wheelchair. The main focus of this study is to validate an effectiveness of our SSSEP-based wheelchair driving system via an online experiment with more challenging tasks than our recent study. In our system, a subject concentrated on one of vibro-tactile stimuli (attached on left-hand, right-hand, and foot) selectively for driving wheelchair (corresponding to turn-left, turn-right, and move-forward). Five healthy subjects participated in the online experiment, and the experimental results show that our SSSEP paradigm is suitable to EEG-based intelligent wheelchair driving system.

Wheelchair Control Based on Steady-State Somatosensory Evoked Potentials

For the last decade, a brain-computer interface (BCI) has gained great interests in the fields and successfully applied to various applications. In this work, we focus on a steady-state somatosensory evoked potential (SSSEP) based brain-controlled wheelchair that allows people with mobility impairment to improve activities of daily living. In our system, a user concentrated on three vibration stimuli (attached on left hand, right-hand, and foot) selectively for control wheelchair. The three stimuli were associated with three commands of wheelchair: turn-left, turn-right, and move-forward. Four healthy subjects participated in wheelchair control experiments to validate performance. As a results, all subjects controlled wheelchair successfully from the start to goal line without any collision.

OpenBMI: A real-time data analysis toolbox for Brain-Machine Interfaces

Recently, there has been an increased demand for Brain-Machine Interface (BMI) toolboxes for neuroscientifc research. In many BMI applications, speller systems can provide an efficient communication channel for users with disabilities. Here, we introduce an open-source BMI toolbox termed ‘OpenBMI’, which supports the various signal processing chains for common BMI paradigms, such as event-related potentials (ERPs) and steady-state visual evoked potentials (SSVEP). The OpenBMI framework consists of ready-to-use experimental paradigms, offline data analysis techniques, online feedback as well as evaluation modules. The data analysis modules provide essential pre-processing steps (segmentation, baseline correction, etc.) as well as signal processing algorithms such as temporal and spatial filtering, artifact rejection, among others. The experimental paradigms of ERP and SSVEP are available with fully open-sourced demo scripts. Users can easily modify or extend the demo scripts for their needs. In this article, the OpenBMI framework, its features as well as its future development plan is introduced.

Analysis of steady state visual evoked potentials based on viewing distance changes for brain-machine interface speller

Recently, steady-state visual evoked potential (SSVEP)-based brain-machine interface (BMI) speller systems have shown a great performance increase with high information transfer rate (ITR) and short response time. In previous BMI speller systems, however, users should utilize the systems at fixed viewing distance environment for evoking SSVEP signals because a variation of the SSVEP signals according to changes of viewing distance was not considered during system design process. For a real-world application of BMI speller, reliable speller systems which are robust to various viewing distance environment are needed. In this study, hence, we investigate the effects of viewing distance on SSVEP by changing distance between a user and visual stimuli. Here, we used four visual stimuli which have different frequencies using LED monitor. In the subsequent analysis, we present classification results with several methods. Our analysis and results show a possibility that SSVEP under various viewing distance environment could be facilitated.

Steady-state visual evoked potential based brain-computer interface with viewing distance changes

In the Brain-Computer Interface (BCI) based on Steady-State Visual Evoked Potential (SSVEP), the viewing distance is usually fixed in the 1 m. However, the performance of SSVEP was decreased when the viewing distance is increasing. This paper aims to investigate the effects of viewing distance variation to the SSVEP. We designed an experimental environment with different viewing distance and generated four visual flicker which presented on LED monitor flickering in frequencies of 7, 8, 9, and 10 Hz. The Canonical correlation analysis (CCA) and event related spectral perturbation (ERSP) methods were used to analyze the frequency information of the acquired SSVEP signals. Our results show that the latencies of SSVEP were increased according to the viewing distances and visual angles. Therefore, we used phase constrained canonical correlation analysis (pCCA) to reflect the latency in viewing distance changes. As a result, the accuracy obtained from pCCA was higher than standard CCA method.

Towards a smart TV control system based on steady-state visual evoked potential

This paper aims to investigate the effects of steady-state visual evoked potential (SSVEP) in the aspects of viewing distance variation for a smart TV control system. We designed an experimental environments with different viewing distance. Four healthy people (age 27.5±1, male) participated in the experiment. Four visual stimuli with a round shape were designed and presented on LED monitor flickering in frequencies of 7, 9, 11, and 13Hz. Viewing distances were changed from 1m to 3m during the experiment. Moreover, we collected SSVEP signals with and without videos as a disturbance stimulus on the monitor to consider similar condition in real life environments. The canonical correlation analysis (CCA) method was used to analyze the frequency information of the acquired SSVEP signals. The averaged accuracies of all subjects were 71 ± 8.7% during without-video condition and 67 ± 14% during with-video condition. The results show necessity of stabilization in SSVEP system performance in environment even with increasing viewing distance.