Effect of time windows in LSTM networks for EEG-based BCIs

Abstract

People with impaired motor function could be helped by an effective brain–computer interface (BCI) based on a real-time electroencephalogram (EEG) and artificial intelligence algorithms. However, current methodologies for interpreting patient instructions from an EEG are not accurate enough to be completely safe in a real-world situation , where a poor decision would place their physical integrity at risk, such as when traveling in an electric wheelchair in a city. For various reasons, such as the low signal-to-noise ratio of portable EEGs or the effects of signal contamination (disturbances due to user movement, temporal variation of the features of EEG signals, etc.), a long short-term memory network (LSTM) (a type of recurrent neural network) that is able to learn data flow patterns from EEG signals could improve the classification of the actions taken by the user. In this paper, the effectiveness of using an LSTM with a low-cost wireless EEG device in real time is tested, and the time window that maximizes its classification accuracy is studied. The goal is to be able to implement it in the BCI of a smart wheelchair with a simple coded command protocol, such as opening or closing the eyes, which could be executed by patients with reduced mobility. Results show a higher resolution of the LSTM with an accuracy range between 77.61 and 92.14% compared to traditional classifiers (59.71%), and an optimal time window of around 7 s for the task done by users in this work. In addition, tests in real-life contexts show that a trade-off between accuracy and response times is necessary to ensure detection.

Brain–Computer Interface Based on PLV-Spatial Filter and LSTM Classification for Intuitive Control of Avatars

“…When this threshold signal identified the areas affected by the flicker, it was corrected by subtracting the weighted component signal of each channel, where the weighting was defined by the ratio between these two signals. For more information on the procedure carried out, see [25].…”

“…The analysis focused on telling an eyes-open state from an eyes-closed state apart. To achieve this differentiation, the alpha band (8-12 Hz) of the EEG signals recorded by the O1 and O2 sensors in the visual area (occipital lobe) [25,35] was used to train the classifier. This band was selected because it is associated with states of visual attention, relaxation, and low cognitive activity [36][37][38].…”

“…An LSTM, an artificial neural network (ANN), was selected as the classification algorithm [43]; the algorithm selection was based on the article [25], where an LSTM network was compared with other classification algorithms. The comparison focused on the classification of the filtered signal from the power spectrum of the sensors corresponding to the occipital lobe, and the device used O1 and O2 sensors.…”

“…The current manuscript presents a practical application of a VR environment that incorporates a BCI system that uses phase-locking value spatial filtering (PLV-SF) [24] and Long Short-Term Memory (LSTM) neural networks for signal classification [25]. In particular, the work of Martín-Chinea et al [24] detailed a spatial filtering based on a graph Laplacian quadratic form, while [25] compared the performance of LSTM neural networks with other classification algorithms commonly used in the literature (support vector machine, discriminant analysis, k-nearest neighbor, and decision tree learner). The LSTM networks showed an improvement range of around 30%.…”

Physiological Noise Filtering in Functional Near-Infrared Spectroscopy Signals Using Wavelet Transform and Long-Short Term Memory Networks

“…In brain research, MODWT has been applied as a preprocessing method for EEG-based seizure detection [ 28 , 44 ], Alzheimer’s diagnosis [ 45 ], and resting state network analysis of fMRI [ 46 ]. Since brain signals are measured in time series, active research on brain signal classification [ 47 , 48 ] uses LSTM. However, to our knowledge, this is the first study to predict the noise in fNIRS signals despite many of the noise components being periodic.…”