Electroencephalography (EEG)-based brain-computer interfaces (BCIs), particularly those using motor-imagery (MI) data, have the potential to become groundbreaking technologies in both clinical and entertainment settings. MI data is generated when a subject imagines the movement of a limb. This paper reviews state-of-the-art signal processing techniques for MI EEG-based BCIs, with a particular focus on the feature extraction, feature selection and classification techniques used. It also summarizes the main applications of EEG-based BCIs, particularly those based on MI data, and finally presents a detailed discussion of the most prevalent challenges impeding the development and commercialization of EEG-based BCIs.

https://www.mdpi.com/1424-8220/19/6/1423/pdf

EEG-Based Brain-Computer Interfaces Using Motor-Imagery: Techniques and Challenges.

Brain-Computer Interface (BCI), in essence, aims at controlling different assistive devices through the utilization of brain waves. It is worth noting that the application of BCI is not limited to medical applications, and hence, the research in this field has gained due attention. Moreover, the significant number of related publications over the past two decades further indicates the consistent improvements and breakthroughs that have been made in this particular field. Nonetheless, it is also worth mentioning that with these improvements, new challenges are constantly discovered. This article provides a comprehensive review of the state-of-the-art of a complete BCI system. First, a brief overview of electroencephalogram (EEG)-based BCI systems is given. Secondly, a considerable number of popular BCI applications are reviewed in terms of electrophysiological control signals, feature extraction, classification algorithms, and performance evaluation metrics. Finally, the challenges to the recent BCI systems are discussed, and possible solutions to mitigate the issues are recommended.

/pdf/current-status-challenges-and-possible-solutions-of-eeg-4el4wek5td.pdf

Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review

Monitoring human brain activity has great potential in helping us understand the functioning of our brain, as well as in preventing mental disorders and cognitive decline and improve our quality of life. Noninvasive surface EEG is the dominant modality for studying brain dynamics and performance in real-life interaction of humans with their environment. To take full advantage of surface EEG recordings, EEG technology has to be advanced to a level that it can be used in daily life activities. Furthermore, users have to see it as an unobtrusive option to monitor and improve their health. To achieve this, EEG systems have to be transformed from stationary, wired, and cumbersome systems used mostly in clinical practice today, to intelligent wearable, wireless, convenient, and comfortable lifestyle solutions that provide high signal quality. Here, we discuss state-of-the-art in wireless and wearable EEG solutions and a number of aspects where such solutions require improvements when handling electrical activity of the brain. We address personal traits and sensory inputs, brain signal generation and acquisition, brain signal analysis, and feedback generation. We provide guidelines on how these aspects can be advanced further such that we can develop intelligent wearable, wireless, lifestyle EEG solutions. We recognized the following aspects as the ones that need rapid research progress: application driven design, end-user driven development, standardization and sharing of EEG data, and development of sophisticated approaches to handle EEG artifacts.

Wearable, Wireless EEG Solutions in Daily Life Applications: What are we Missing?

The vigilance of the driver is important for railway safety, despite not being included in the safety management system (SMS) for high-speed train safety. In this paper, a novel fatigue detection system for high-speed train safety based on monitoring train driver vigilance using a wireless wearable electroencephalograph (EEG) is presented. This system is designed to detect whether the driver is drowsiness. The proposed system consists of three main parts: (1) a wireless wearable EEG collection; (2) train driver vigilance detection; and (3) early warning device for train driver. In the first part, an 8-channel wireless wearable brain-computer interface (BCI) device acquires the locomotive driver’s brain EEG signal comfortably under high-speed train-driving conditions. The recorded data are transmitted to a personal computer (PC) via Bluetooth. In the second step, a support vector machine (SVM) classification algorithm is implemented to determine the vigilance level using the Fast Fourier transform (FFT) to extract the EEG power spectrum density (PSD). In addition, an early warning device begins to work if fatigue is detected. The simulation and test results demonstrate the feasibility of the proposed fatigue detection system for high-speed train safety.

https://www.mdpi.com/1424-8220/17/3/486/pdf

Design of a Fatigue Detection System for High-Speed Trains Based on Driver Vigilance Using a Wireless Wearable EEG

A brain-computer interface (BCI) provides a direct communication channel between a brain and an external device. Steady-state visual evoked potential based BCI (SSVEP-BCI) has received increasing attention due to its high information transfer rate, which is accomplished by individual calibration for frequency recognition. Task-related component analysis (TRCA) is a recent and state-of-the-art method for individually calibrated SSVEP-BCIs. However, in TRCA, the spatial filter learned from each stimulus may be redundant and temporal information is not fully utilized. To address this issue, this paper proposes a novel method, i.e., task-discriminant component analysis (TDCA), to further improve the performance of individually calibrated SSVEP-BCI. The performance of TDCA was evaluated by two publicly available benchmark datasets, and the results demonstrated that TDCA outperformed ensemble TRCA and other competing methods by a significant margin. An offline and online experiment testing 12 subjects further validated the effectiveness of TDCA. The present study provides a new perspective for designing decoding methods in individually calibrated SSVEP-BCI and presents insight for its implementation in high-speed brain speller applications.

/pdf/improving-the-performance-of-individually-calibrated-ssvep-5g84du4ssk.pdf

Improving the Performance of Individually Calibrated SSVEP-BCI by Task- Discriminant Component Analysis

Recent technological advances in the field of skin electrodes and on-body sensors indicate a possibility of having an alternative to the traditionally used conductive gel electrodes for measuring electrical signals of the brain (electroencephalogram, EEG). This paper evaluates whether water-based and dry contact electrode solutions can replace the gel ones. The quality of the obtained signal by three headsets, each using 8 electrodes of a different type, is estimated onthe steady state visual evoked potential (SSVEP) brain-computer interface (BCI) use case. The stimuli frequencies in the low (12 to 21Hz) and high (28 to 40Hz) frequency domain were used. Six people, that had different hair length and type, participated in the experiment. SSVEP response in terms of power spectra across different electrodes is compared and the impact of noise on temporal characteristics ofthe response is discussed. For people with shorter hair style the performance of water-based and dry electrodes comes close to the gelones in the optimal setting. On average, the classification accuracy of 0.63 for dry and 0.88 for water-based electrodes is achieved, compared to the 0.96 obtained for gel electrodes. The theoretical maximum of the average information transfer rate across participants was 23bpm for dry, 38bpm for water-based and 67bpm for gel electrodes. Furthermore, the convenience level of all three setups was seen as comparable. These results demonstrate that, having optimized headset and electrode design for dry and water-based electrodes for people with different hair length and type, dry and water-based electrode scan replace gel ones in BCIs and Neurofeedback applications where lower communication speed is acceptable.

To what extent can dry and water-based EEG electrodes replace conductive gel ones?: A Steady State Visual Evoked Potential Brain-Computer Interface Case Study

To What Extent can Dry and Water-based EEG Electrodes Replace Conductive Gel Ones? - A Steady State Visual Evoked Potential Brain-computer Interface Case Study.

This paper evaluates whether water-based and dry contact electrode solutions can replace the gel ones in measuring electrical brain activity by the electroencephalogram (EEG) The quality of the signals measured by three setups (dry, water, and gel), each using 8 electrodes, is estimated for the case of a brain-computer interface (BCI) based on steady state visual evoked potential (SSVEP) Repetitive visual stimuli in the low (12 to 21Hz) and high (28 to 40Hz) frequency ranges were applied Six people, that had different hair length and type, participated in the experiment For people with shorter hair style the performance of water-based and dry electrodes comes close to the gel ones in the optimal setting On average, the classification accuracy of 063 for dry and 088 for water-based electrodes is achieved, compared to the 096 obtained for gel electrodes The theoretical maximum of the average information transfer rate across participants was 23bpm for dry, 38bpm for water-based and 67bpm for gel electrodes Furthermore, the convenience level of all three setups was seen as comparable These results demonstrate that, having optimized headset and electrode design, dry and water-based electrodes can replace gel ones in BCI applications where lower communication speed is acceptable

Dry and Water-Based EEG Electrodes in SSVEP-Based BCI Applications

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To what extent can dry and water-based EEG electrodes replace conductive gel ones?: A Steady State Visual Evoked Potential Brain-Computer Interface Case Study

To What Extent can Dry and Water-based EEG Electrodes Replace Conductive Gel Ones? - A Steady State Visual Evoked Potential Brain-computer Interface Case Study.

Dry and Water-Based EEG Electrodes in SSVEP-Based BCI Applications