A Novel Event-Related Potential-Based Brain–Computer Interface for Continuously Controlling Dynamic Systems

Lian, Jinling; Bi, Luzheng; Fei, Weijie

doi:10.1109/access.2019.2905669

Cited by 12 publications

(10 citation statements)

References 30 publications

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“…Human-specific parameters such as fatigue and concentration change during the operation of the BCI as well as on a day-to-day basis, and the characteristics of the ERP change accordingly. This is one of the central challenges of the ML methods, To deal with this challenge, an increasing number of studies have been conducted on robust universal feature extraction methods such as deep learning [17], [18]. However, if the parameters of the MLbased classifier are fixed after the initial training, it is not possible to mitigate the effects of these occasional changes.…”

Section: Introductionmentioning

confidence: 99%

Semi-Supervised Learning for Auditory Event-Related Potential-Based Brain–Computer Interface

et al. 2021

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A brain-computer interface (BCI) is a communication tool that analyzes neural activity and relays the translated commands to carry out actions. In recent years, semi-supervised learning (SSL) has attracted attention for visual event-related potential (ERP)-based BCIs and motor-imagery BCIs as an effective technique that can adapt to the variations in patterns among subjects and trials. The applications of the SSL techniques are expected to improve the performance of auditory ERP-based BCIs as well. However, there is no conclusive evidence supporting the positive effect of SSL techniques on auditory ERP-based BCIs. If the positive effect could be verified, it will be helpful for the BCI community. In this study, we assessed the effects of SSL techniques on two public auditory BCI datasets-AMUSE and PASS2Dusing the following machine learning algorithms: step-wise linear discriminant analysis, shrinkage linear discriminant analysis, spatial temporal discriminant analysis, and least-squares support vector machine. These backbone classifiers were firstly trained by labeled data and incrementally updated by unlabeled data in every trial of testing data based on SSL approach. Although a few data of the datasets were negatively affected, most data were apparently improved by SSL in all cases. The overall accuracy was logarithmically increased with every additional unlabeled data. This study supports the positive effect of SSL techniques and encourages future researchers to apply them to auditory ERP-based BCIs.

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Section: Introductionmentioning

confidence: 99%

Semi-Supervised Learning for Auditory Event-Related Potential-Based Brain–Computer Interface

et al. 2021

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“…Among these, EEG is most widely used because of its advantages over the other modalities, e.g., its cost-effectiveness, high temporal resolution, and portability [13,14]. Over the past decades, neuroscientists have developed various BCI paradigms based on specific EEG signal patterns, such as the steady-state visual evoked potential (SSVEP) [15][16][17][18][19], auditory steady-state response [20,21], event-related potential [5,[22][23][24], slow cortical potential [25], and event-related 2 of 13 synchronization/desynchronization [26,27]. These BCI paradigms can allow patients in a locked-in state to communicate.…”

Section: Introductionmentioning

confidence: 99%

Development of a Brain–Computer Interface Toggle Switch with Low False-Positive Rate Using Respiration-Modulated Photoplethysmography

Han

Kim

2020

Sensors

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Asynchronous brain–computer interfaces (BCIs) based on electroencephalography (EEG) generally suffer from poor performance in terms of classification accuracy and false-positive rate (FPR). Thus, BCI toggle switches based on electrooculogram (EOG) signals were developed to toggle on/off synchronous BCI systems. The conventional BCI toggle switches exhibit fast responses with high accuracy; however, they have a high FPR or cannot be applied to patients with oculomotor impairments. To circumvent these issues, we developed a novel BCI toggle switch that users can employ to toggle on or off synchronous BCIs by holding their breath for a few seconds. Two states—normal breath and breath holding—were classified using a linear discriminant analysis with features extracted from the respiration-modulated photoplethysmography (PPG) signals. A real-time BCI toggle switch was implemented with calibration data trained with only 1-min PPG data. We evaluated the performance of our PPG switch by combining it with a steady-state visual evoked potential-based BCI system that was designed to control four external devices, with regard to the true-positive rate and FPR. The parameters of the PPG switch were optimized through an offline experiment with five subjects, and the performance of the switch system was evaluated in an online experiment with seven subjects. All the participants successfully turned on the BCI by holding their breath for approximately 10 s (100% accuracy), and the switch system exhibited a very low FPR of 0.02 false operations per minute, which is the lowest FPR reported thus far. All participants could successfully control external devices in the synchronous BCI mode. Our results demonstrated that the proposed PPG-based BCI toggle switch can be used to implement practical BCIs.

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“…Signal processing is an essential part of BCI systems because it helps to extract meaningful information from brain signals. The basic BCI module includes three main steps, which are the preprocessing, feature extraction and classification, concerned with the identification of different mental states (Lian et al 2019). Among these, preprocessing is the step which requires special attention.…”

Section: Introductionmentioning

confidence: 99%

A new evolutionary preprocessing approach for classification of mental arithmetic based EEG signals

Ergün

Aydemir

2020

Cogn Neurodyn

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Brain computer interface systems decode brain activities from electroencephalogram (EEG) signals and translate the user's intentions into commands to control and/or communicate with augmentative or assistive devices without activating any muscle or peripheral nerve. In this paper, we aimed to improve the accuracy of these systems using improved EEG signal processing techniques through a novel evolutionary approach (fusion-based preprocessing method). This approach was inspired by chromosomal crossover, which is the transfer of genetic material between homologous chromosomes. In this study, the proposed fusion-based preprocessing method was applied to an open access dataset collected from 29 subjects. Then, features were extracted by the autoregressive model and classified by k-nearest neighbor classifier. We achieved classification accuracy (CA) ranging from 67.57 to 99.70% for the detection of binary mental arithmetic (MA) based EEG signals. In addition to obtaining an average CA of 88.71%, 93.10% of the subjects showed performance improvement using the fusion-based preprocessing method. Furthermore, we compared the proposed study with the common average reference (CAR) method and without applying any preprocessing method. The achieved results showed that the proposed method provided 3.91% and 2.75% better CA then the CAR and without applying any preprocessing method, respectively. The results also prove that the proposed evolutionary preprocessing approach has great potential to classify the EEG signals recorded during MA task.

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A Novel Event-Related Potential-Based Brain–Computer Interface for Continuously Controlling Dynamic Systems

Cited by 12 publications

References 30 publications

Semi-Supervised Learning for Auditory Event-Related Potential-Based Brain–Computer Interface

Semi-Supervised Learning for Auditory Event-Related Potential-Based Brain–Computer Interface

Development of a Brain–Computer Interface Toggle Switch with Low False-Positive Rate Using Respiration-Modulated Photoplethysmography

A new evolutionary preprocessing approach for classification of mental arithmetic based EEG signals

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