Adapting Artifact Subspace Reconstruction Method for SingleChannel EEG using Signal Decomposition Techniques.

Artifact removal from electroencephalography (EEG) data is a crucial step in the analysis of neural signals. One method that has been gaining popularity in recent years is Artifact Subspace Reconstruction (ASR), which is highly effective in eliminating large amplitude and transient artifacts in EEG data. However, traditional ASR is not possible to use with single-channel EEG data. In this study, we propose incorporating signal decomposition techniques such as ensemble empirical mode decomposition (EEMD), wavelet transform (WT), and singular spectrum analysis (SSA) into ASR, to decompose single-channel data into multiple components. This allows the ASR process to be applied effectively to the data. Our results show that the proposed single-channel version of ASR outperforms its counterpart Independent Component Analysis (ICA) methods when tested on two open datasets. Our findings indicate that ASR has significant potential as a powerful tool for removing artifacts from EEG data analysis.Clinical Relevance- This provided artifact removal technique for single-channel EEG.

Decoding auditory-evoked response in affective states using wearable around-ear EEG system.

Objective.In this paper, an around-ear EEG system is investigated as an alternative methodology to conventional scalp-EEG-based systems in classifying human affective states in the arousal-valence domain evoked in response to auditory stimuli.Approach.EEG recorded from around the ears is compared to EEG collected according to the international 10-20 system in terms of efficacy in an affective state classification task. A wearable device with eight dry EEG channels is designed for ear-EEG acquisition in this study. Twenty-one subjects participated in an experiment consisting of six sessions over three days using both ear and scalp-EEG acquisition methods. Experimental tasks consisted of listening to an auditory stimulus and self-reporting the elicited emotion in response to the said stimulus. Various features were used in tandem with asymmetry methods to evaluate binary classification performances of arousal and valence states using ear-EEG signals in comparison to scalp-EEG.Main results.We achieve an average accuracy of 67.09% ± 6.14 for arousal and 66.61% ± 6.14 for valence after training a multi-layer extreme learning machine with ear-EEG signals in a subject-dependent context in comparison to scalp-EEG approach which achieves an average accuracy of 68.59% ± 6.26 for arousal and 67.10% ± 4.99 for valence. In a subject-independent context, the ear-EEG approach achieves 63.74% ± 3.84 for arousal and 64.32% ± 6.38 for valence while the scalp-EEG approach achieves 64.67% ± 6.91 for arousal and 64.86% ± 5.95 for valence. The best results show no significant differences between ear-EEG and scalp-EEG signals for classifications of affective states.Significance.To the best of our knowledge, this paper is the first work to explore the use of around-ear EEG signals in emotion monitoring. Our results demonstrate the potential use of around-ear EEG systems for the development of emotional monitoring setups that are more suitable for use in daily affective life log systems compared to conventional scalp-EEG setups.

Neural Applications Using Immersive Virtual Reality: A Review on EEG Studies.

Recent advancements in immersive virtual reality head-mounted displays allowed users to better engage with simulated graphical environments. Having the screen egocentrically stabilized in a way such that the users may freely rotate their heads to observe virtual surroundings, head-mounted displays present virtual scenarios with rich immersion. With such an enhanced degree of freedom, immersive virtual reality displays have also been integrated with electroencephalograms, which make it possible to study and utilize brain signals non-invasively, to analyze and apply their capabilities. In this review, we introduce recent progress that utilized immersive head-mounted displays along with electroencephalograms across various fields, focusing on the purposes and experimental designs of their studies. The paper also highlights the effects of using immersive virtual reality discovered through the electroencephalogram analysis and discusses existing limitations, current trends as well as future research opportunities that may hopefully act as a useful source of information for further improvement of electroencephalogram-based immersive virtual reality applications.

The future of wearable EEG: a review of ear-EEG technology and its applications.

Objective.This review paper provides a comprehensive overview of ear-electroencephalogram (EEG) technology, which involves recording EEG signals from electrodes placed in or around the ear, and its applications in the field of neural engineering.Approach.We conducted a thorough literature search using multiple databases to identify relevant studies related to ear-EEG technology and its various applications. We selected 123 publications and synthesized the information to highlight the main findings and trends in this field.Main results.Our review highlights the potential of ear-EEG technology as the future of wearable EEG technology. We discuss the advantages and limitations of ear-EEG compared to traditional scalp-based EEG and methods to overcome those limitations. Through our review, we found that ear-EEG is a promising method that produces comparable results to conventional scalp-based methods. We review the development of ear-EEG sensing devices, including the design, types of sensors, and materials. We also review the current state of research on ear-EEG in different application areas such as brain-computer interfaces, and clinical monitoring.Significance.This review paper is the first to focus solely on reviewing ear-EEG research articles. As such, it serves as a valuable resource for researchers, clinicians, and engineers working in the field of neural engineering. Our review sheds light on the exciting future prospects of ear-EEG, and its potential to advance neural engineering research and become the future of wearable EEG technology.

A novel online BCI system using speech imagery and ear-EEG for home appliances control.

BACKGROUND AND OBJECTIVE: This paper investigates a novel way to interact with home appliances via a brain-computer interface (BCI), using electroencephalograph (EEG) signals acquired from around the user's ears with a custom-made wearable BCI headphone. METHODS: The users engage in speech imagery (SI), a type of mental task where they imagine speaking out a specific word without producing any sound, to control an interactive simulated home appliance. In this work, multiple models are employed to improve the performance of the system. Temporally-stacked multi-band covariance matrix (TSMBC) method is used to represent the neural activities during SI tasks with spatial, temporal, and spectral information included. To further increase the usability of our proposed system in daily life, a calibration session, where the pre-trained models are fine-tuned, is added to maintain performance over time with minimal training. Eleven participants were recruited to evaluate our method over three different sessions: a training session, a calibration session, and an online session where users were given the freedom to achieve a given goal on their own. RESULTS: In the offline experiment, all participants were able to achieve a classification accuracy significantly higher than the chance level. In the online experiments, a few participants were able to use the proposed system to freely control the home appliance with high accuracy and relatively fast command delivery speed. The best participant achieved an average true positive rate and command delivery time of 0.85 and 3.79 s/command, respectively. CONCLUSION: Based on the positive experimental results and user surveys, the novel ear-EEG-SI-based BCI paradigm is a promising approach for the wearable BCI system for daily life.

Speech-imagery-based brain-computer interface system using ear-EEG.

OBJECTIVE: This study investigates the efficacy of electroencephalography (EEG) centered around the user's ears (ear-EEG) for a speech-imagery-based brain-computer interface (BCI) system. APPROACH: A wearable ear-EEG acquisition tool was developed and its performance was directly compared to that of a conventional 32-channel scalp-EEG setup in a multi-class speech imagery classification task. Riemannian tangent space projections of EEG covariance matrices were used as input features to a multi-layer extreme learning machine classifier. Ten subjects participated in an experiment consisting of six sessions spanning three days. The experiment involves imagining four speech commands ('Left,' 'Right,' 'Forward,' and 'Go back') and staying in a rest condition. MAIN RESULTS: The classification accuracy of our system is significantly above the chance level (20%). The classification result averaged across all ten subjects is 38.2% and 43.1% with a maximum (max) of 43.8% and 55.0% for ear-EEG and scalp-EEG, respectively. According to an analysis of variance, seven out of ten subjects show no significant difference between the performance of ear-EEG and scalp-EEG. SIGNIFICANCE: To our knowledge, this is the first study that investigates the performance of ear-EEG in a speech-imagery-based BCI. The results indicate that ear-EEG has great potential as an alternative to the scalp-EEG acquisition method for speech-imagery monitoring. We believe that the merits and feasibility of both speech imagery and ear-EEG acquisition in the proposed system will accelerate the development of the BCI system for daily-life use.

A novel hybrid auditory BCI paradigm combining ASSR and P300.

BACKGROUND: Brain-computer interface (BCI) is a technology that provides an alternative way of communication by translating brain activities into digital commands. Due to the incapability of using the vision-dependent BCI for patients who have visual impairment, auditory stimuli have been used to substitute the conventional visual stimuli. NEW METHOD: This paper introduces a hybrid auditory BCI that utilizes and combines auditory steady state response (ASSR) and spatial-auditory P300 BCI to improve the performance for the auditory BCI system. The system works by simultaneously presenting auditory stimuli with different pitches and amplitude modulation (AM) frequencies to the user with beep sounds occurring randomly between all sound sources. Attention to different auditory stimuli yields different ASSR and beep sounds trigger the P300 response when they occur in the target channel, thus the system can utilize both features for classification. RESULTS: The proposed ASSR/P300-hybrid auditory BCI system achieves 85.33% accuracy with 9.11 bits/min information transfer rate (ITR) in binary classification problem. COMPARISON WITH EXISTING METHODS: The proposed system outperformed the P300 BCI system (74.58% accuracy with 4.18 bits/min ITR) and the ASSR BCI system (66.68% accuracy with 2.01 bits/min ITR) in binary-class problem. The system is completely vision-independent. CONCLUSIONS: This work demonstrates that combining ASSR and P300 BCI into a hybrid system could result in a better performance and could help in the development of the future auditory BCI.