Abstract
BackgroundWhen an unexpected perturbation in the environment occurs, the subsequent alertness state may cause a brain activation responding to that perturbation which can be detected and employed by a Brain-Computer Interface (BCI). In this work, the possibility of detecting a sudden obstacle appearance analyzing electroencephalographic (EEG) signals is assessed. For this purpose, different features of EEG signals are evaluated during the appearance of sudden obstacles while a subject is walking on a treadmill. The future goal is to use this procedure to detect any obstacle appearance during walking when the user is wearing a lower limb exoskeleton in order to generate an emergency stop command for the exoskeleton. This would enhance the user-exoskeleton interaction, improving the safety mechanisms of current exoskeletons.MethodsIn order to detect the change in the brain activity when an obstacle suddenly appears, different features of EEG signals are evaluated using the recordings of five healthy subjects. Since the change in the brain activity occurs in the time domain, the features evaluated are: common spatial patterns, average power, slope, and the coefficients of a polynomial fit. A Linear Discriminant Analysis-based classifier is used to differentiate between two conditions: the appearance or not of an obstacle. The evaluation of the performance to detect the obstacles is made in terms of accuracy, true positive (TP) and false positive (FP) rates.ResultsFrom the offline analysis, the best performance is achieved when the slope or the polynomial coefficients are used as features, with average detection accuracy rates of 74.0 and 79.5 %, respectively. These results are consistent with the pseudo-online results, where a complete EEG recording is segmented into windows of 500 ms and overlapped 400 ms, and a decision about the obstacle appearance is made for each window. The results of the best subject were 11 out of 14 obstacles detected with a rate of 9.09 FPs/min, and 10 out of 14 obstacles detected with a rate of 6.34 FPs/min using slope and polynomial coefficients features, respectively.ConclusionsAn EEG-based BCI can be developed to detect the appearance of unexpected obstacles. The average accuracy achieved is 79.5 % of success rate with a low number of false detections. Thus, the online performance of the BCI would be suitable for commanding in a safely way a lower limb exoskeleton during walking.
Highlights
When an unexpected perturbation in the environment occurs, the subsequent alertness state may cause a brain activation responding to that perturbation which can be detected and employed by a Brain-Computer Interface (BCI)
A digital bandpass filter from 0.4 to 3 Hz is applied. This filter was selected on the basis of our previous work [19], where we found that changes in the EEG signals due the emergence of obstacles appear in low frequencies
Offline analysis Since the change of the EEG activity is focussed over the fronto-central area, the features described above were extracted using the channels FC1, FCz, FC2, C1, Cz, C2, except in the case of Common Spatial Patterns (CSP), where all the channels were used
Summary
When an unexpected perturbation in the environment occurs, the subsequent alertness state may cause a brain activation responding to that perturbation which can be detected and employed by a Brain-Computer Interface (BCI). The early CNV reflects the processing of the warning signal and the anticipation of the upcoming event, the late CNV involves neural activity prior to reaction [3, 4] These changes in the brain activity, which can be recorded in a non-invasive way through electroencephalography (EEG), can be used to develop a BrainComputer Interface (BCI) to establish a communication pathway between a subject and a device without the use of peripheral nerves [5, 6]. It has been demonstrated that information about locomotion can be extracted from the primary motor cortex [9], and the first efforts to decode gait parameters from EEG recordings are being developed [10, 11] In this sense, BCIs have started to be used in the rehabilitation of lower limbs, being used to command exoskeletons. If the obstacle were detected through EEG signals, the user wold be able to volitionally discriminate if the obstacle is dangerous or not
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