Abstract
This work presents the design, implementation, and evaluation of a P300-based brain-machine interface (BMI) developed to control a robotic hand-orthosis. The purpose of this system is to assist patients with amyotrophic lateral sclerosis (ALS) who cannot open and close their hands by themselves. The user of this interface can select one of six targets, which represent the flexion-extension of one finger independently or the movement of the five fingers simultaneously. We tested offline and online our BMI on eighteen healthy subjects (HS) and eight ALS patients. In the offline test, we used the calibration data of each participant recorded in the experimental sessions to estimate the accuracy of the BMI to classify correctly single epochs as target or non-target trials. On average, the system accuracy was 78.7% for target epochs and 85.7% for non-target trials. Additionally, we observed significant P300 responses in the calibration recordings of all the participants, including the ALS patients. For the BMI online test, each subject performed from 6 to 36 attempts of target selections using the interface. In this case, around 46% of the participants obtained 100% of accuracy, and the average online accuracy was 89.83%. The maximum information transfer rate (ITR) observed in the experiments was 52.83 bit/min, whereas that the average ITR was 18.13 bit/min. The contributions of this work are the following. First, we report the development and evaluation of a mind-controlled robotic hand-orthosis for patients with ALS. To our knowledge, this BMI is one of the first P300-based assistive robotic devices with multiple targets evaluated on people with ALS. Second, we provide a database with calibration data and online EEG recordings obtained in the evaluation of our BMI. This data is useful to develop and compare other BMI systems and test the processing pipelines of similar applications.
Highlights
Since the early developments of brain-machine interface (BMI), one of the most promising applications of this technology is the use of neuroprosthetic devices to assist people with reduced mobility
We presented the development and evaluation of a P300-based BMI coupled with a robotic hand-orthosis
Our system is able to perform the thumb opposition movement or movements with any combination of fingers, we can configure the orthosis to be initially closed and perform the extension-flexion of the fingers, the initial position and angular range of the movements of the orthosis can be controlled, this allows to adapt the system to the individual characteristics of the users, for this initial evaluation, we wanted to test the general performance of the interface at the most individual level and with the most complex movement, having a total of six possible movements
Summary
Since the early developments of BMIs, one of the most promising applications of this technology is the use of neuroprosthetic devices to assist people with reduced mobility. There is a consensus among researchers of this area that BMIs may significantly improve the lives of patients who suffer neuromuscular disorders such as ALS. There are various techniques to register brain signals, but the non-invasive neuroimage modality most widely used in BMI applications is electroencephalography (EEG) because of its high temporal resolution, low cost, and mobility (Flores et al, 2018; Xiao et al, 2019). Among EEG-based BMIs, the P300 paradigm is one of the most popular techniques for building applications with multiple options because it allows achieving high accuracies without the need for long calibration sessions (Hwang et al, 2013; De Venuto et al, 2018). P300-based BMIs have higher bit rates than motor imagery interfaces, while the stimulation technique for evoking P300 potentials is less visually fatiguing than the method used to elicit steady-state visually evoked potentials (Cattan et al, 2019)
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