OBJECTIVE: To assess the feasibility and robustness of an asynchronous and non-invasive EEG-based Brain-Computer Interface (BCI) for continuous mental control of a wheelchair. METHODS: In experiment 1 two subjects were asked to mentally drive both a real and a simulated wheelchair from a starting point to a goal along a pre-specified path. Here we only report experiments with the simulated wheelchair for which we have extensive data in a complex environment that allows a sound analysis. Each subject participated in five experimental sessions, each consisting of 10 trials. The time elapsed between two consecutive experimental sessions was variable (from 1h to 2months) to assess the system robustness over time. The pre-specified path was divided into seven stretches to assess the system robustness in different contexts. To further assess the performance of the brain-actuated wheelchair, subject 1 participated in a second experiment consisting of 10 trials where he was asked to drive the simulated wheelchair following 10 different complex and random paths never tried before. RESULTS: In experiment 1 the two subjects were able to reach 100% (subject 1) and 80% (subject 2) of the final goals along the pre-specified trajectory in their best sessions. Different performances were obtained over time and path stretches, what indicates that performance is time and context dependent. In experiment 2, subject 1 was able to reach the final goal in 80% of the trials. CONCLUSIONS: The results show that subjects can rapidly master our asynchronous EEG-based BCI to control a wheelchair. Also, they can autonomously operate the BCI over long periods of time without the need for adaptive algorithms externally tuned by a human operator to minimize the impact of EEG non-stationarities. This is possible because of two key components: first, the inclusion of a shared control system between the BCI system and the intelligent simulated wheelchair; second, the selection of stable user-specific EEG features that maximize the separability between the mental tasks. SIGNIFICANCE: These results show the feasibility of continuously controlling complex robotics devices using an asynchronous and non-invasive BCI.
OBJECTIVE: To assess the feasibility and robustness of an asynchronous and non-invasive EEG-based Brain-Computer Interface (BCI) for continuous mental control of a wheelchair. METHODS: In experiment 1 two subjects were asked to mentally drive both a real and a simulated wheelchair from a starting point to a goal along a pre-specified path. Here we only report experiments with the simulated wheelchair for which we have extensive data in a complex environment that allows a sound analysis. Each subject participated in five experimental sessions, each consisting of 10 trials. The time elapsed between two consecutive experimental sessions was variable (from 1h to 2months) to assess the system robustness over time. The pre-specified path was divided into seven stretches to assess the system robustness in different contexts. To further assess the performance of the brain-actuated wheelchair, subject 1 participated in a second experiment consisting of 10 trials where he was asked to drive the simulated wheelchair following 10 different complex and random paths never tried before. RESULTS: In experiment 1 the two subjects were able to reach 100% (subject 1) and 80% (subject 2) of the final goals along the pre-specified trajectory in their best sessions. Different performances were obtained over time and path stretches, what indicates that performance is time and context dependent. In experiment 2, subject 1 was able to reach the final goal in 80% of the trials. CONCLUSIONS: The results show that subjects can rapidly master our asynchronous EEG-based BCI to control a wheelchair. Also, they can autonomously operate the BCI over long periods of time without the need for adaptive algorithms externally tuned by a human operator to minimize the impact of EEG non-stationarities. This is possible because of two key components: first, the inclusion of a shared control system between the BCI system and the intelligent simulated wheelchair; second, the selection of stable user-specific EEG features that maximize the separability between the mental tasks. SIGNIFICANCE: These results show the feasibility of continuously controlling complex robotics devices using an asynchronous and non-invasive BCI.
Authors: Saugat Bhattacharyya; Abhronil Sengupta; Tathagatha Chakraborti; Amit Konar; D N Tibarewala Journal: Med Biol Eng Comput Date: 2013-10-29 Impact factor: 2.602
Authors: Bradley J Edelman; Jianjun Meng; Nicholas Gulachek; Christopher C Cline; Bin He Journal: IEEE Trans Neural Syst Rehabil Eng Date: 2018-05 Impact factor: 3.802