Real-time EEG classification via coresets for BCI applications

Abstract

A brain-computer interface (BCI) based on the motor imagery (MI) paradigm translates one's motor intention into a control signal by classifying the Electroencephalogram (EEG) signal of different tasks. However, most existing systems either (i) use a high-quality algorithm to train the data off-line and run only classification in real-time, since the off-line algorithm is too slow, or (ii) use lowquality heuristics that are sufficiently fast for real-time training but introduces relatively large classification error.In this work, we propose a novel processing pipeline that allows real-time and parallel learning of EEG signals using high-quality but possibly inefficient algorithms. This is done by forging a link between BCI and core-sets, a technique that originated in computational geometry for handling streaming data via data summarization.We suggest an algorithm that maintains the representation such coreset tailored to handle the EEG signal which enables: (i) real time and continuous computation of the Common Spatial Pattern (CSP) feature extraction method on a coreset representation of the signal (instead on the signal itself) , (ii) improvement of the CSP algorithm efficiency with provable guarantees by applying CSP algorithm on the coreset, and (iii) real time addition of the data trials (EEG data windows) to the coreset.For simplicity, we focus on the CSP algorithm, which is a classic algorithm.Nevertheless, we expect that our coreset will be extended to other algorithms in future papers. In the experimental results we show that our system can indeed learn EEG signals in real-time for example a 64 channels setup with hundreds of time samples per second. Full open source is provided to reproduce the experiment and in the hope that it will be used and extended to more coresets and BCI applications in the future.

Brain Computer Interface-EEG based Imagined Word Prediction Using Convolutional Neural Network Visual Stimuli for Speech Disability

“…Also, to be inferred is that when the number of extracted features used is decreased from 7 to 4, all the performance metrics decreased in both the trained models, however, the performances decreased by 3.3% on average. 9 shows the comparison of the developed models with the existing models presented in the literature [31], [32]. It could be observed that the accuracy of the developed model 1 has been improved and the percentage of improvement is 3.79%, 6.71%, and 27.90%, as compared with model 2 (Googlenet), [31] and [32].…”

“…9 shows the comparison of the developed models with the existing models presented in the literature [31], [32]. It could be observed that the accuracy of the developed model 1 has been improved and the percentage of improvement is 3.79%, 6.71%, and 27.90%, as compared with model 2 (Googlenet), [31] and [32]. It can be noted that the number of features extracted has been reduced and the system or models developed is analyzed and the performance metrics are evaluated.…”

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

“…And finally, as a generic conclusion, despite the good results obtained with the EEG, there are other methods, such as EMG (Usman et al 2021 ) or video eye tracking (Ibrahim et al 2021 ) that could obtain better results in the proposed task. But we must not forget the potential of the system proposed here, which can be applied to other tasks such as providing feedback to aid with motor diseases (Miao et al 2020 ; Pimenta et al 2021 ), neuromarketing (Gers et al 2000 ) or the classification of more elaborate brain tasks (Pfurtscheller et al 1998 ; Kumar et al 2019 ; Netzer et al 2020 ) (to which the same study conducted in this work can be applied).…”

“…Real-time execution poses additional problems. The first one, as Netzer et al defined in their paper, is a common issue in BCI processing, namely that the probability of success in the detection depends on capturing time (Netzer et al 2020 ). If the BCI has short reaction time, the classification algorithm will perform worse, while if a better algorithm is used, the reaction time increases considerably.…”

Investigation of brain computer interface for rich multimedia environment

“…Netzer et al 6 proposed a processing pipeline for MI‐BCI framework through cloud. The proposed coreset technique handles streaming data through data summarization.…”