Hohyun Cho

High theta and low alpha powers may be indicative of BCI-illiteracy in motor imagery.

In most brain computer interface (BCI) systems, some target users have significant difficulty in using BCI systems. Such target users are called ‘BCI-illiterate’. This phenomenon has been poorly investigated, and a clear understanding of the BCI-illiteracy mechanism or a solution to this problem has not been reported to date. In this study, we sought to demonstrate the neurophysiological differences between two groups (literate, illiterate) with a total of 52 subjects. We investigated recordings under non-task related state (NTS) which is collected during subject is relaxed with eyes open. We found that high theta and low alpha waves were noticeable in the BCI-illiterate relative to the BCI-literate people. Furthermore, these high theta and low alpha wave patterns were preserved across different mental states, such as NTS, resting before motor imagery (MI), and MI states, even though the spatial distribution of both BCI-illiterate and BCI-literate groups did not differ. From these findings, an effective strategy for pre-screening subjects for BCI illiteracy has been determined, and a performance factor that reflects potential user performance has been proposed using a simple combination of band powers. Our proposed performance factor gave an r = 0.59 (r2 = 0.34) in a correlation analysis with BCI performance and yielded as much as r = 0.70 (r2 = 0.50) when seven outliers were rejected during the evaluation of whole data (N = 61), including BCI competition datasets (N = 9). These findings may be directly applicable to online BCI systems.

Gamma band activity associated with BCI performance: simultaneous MEG/EEG study.

While brain computer interface (BCI) can be employed with patients and healthy subjects, there are problems that must be resolved before BCI can be useful to the public. In the most popular motor imagery (MI) BCI system, a significant number of target users (called “BCI-Illiterates”) cannot modulate their neuronal signals sufficiently to use the BCI system. This causes performance variability among subjects and even among sessions within a subject. The mechanism of such BCI-Illiteracy and possible solutions still remain to be determined. Gamma oscillation is known to be involved in various fundamental brain functions, and may play a role in MI. In this study, we investigated the association of gamma activity with MI performance among subjects. Ten simultaneous MEG/EEG experiments were conducted; MI performance for each was estimated by EEG data, and the gamma activity associated with BCI performance was investigated with MEG data. Our results showed that gamma activity had a high positive correlation with MI performance in the prefrontal area. This trend was also found across sessions within one subject. In conclusion, gamma rhythms generated in the prefrontal area appear to play a critical role in BCI performance.

Noise robustness analysis of sparse representation based classification method for non-stationary EEG signal classification

Abstract In the electroencephalogram (EEG)-based brain–computer interface (BCI) systems, classification is an important signal processing step to control external devices using brain activity. However, scalp-recorded EEG signals have inherent non-stationary characteristics; thus, the classification performance is deteriorated by changing the background activity of the EEG during the BCI experiment. Recently, the sparse representation based classification (SRC) method has shown a robust classification performance in many pattern recognition fields including BCI. In this study, we aim to analyze noise robustness of the SRC method to evaluate the capability of the SRC for non-stationary EEG signal classification. For this purpose, we generate noisy test signals by adding a noise source such as random Gaussian and scalp-recorded background noise into the original motor imagery based EEG signals. Using the noisy test signals and real online-experimental dataset, we compare the classification performance of the SRC and support vector machine (SVM). Furthermore, we analyze the unique classification mechanism of the SRC. We observed that the SRC method provided better classification accuracy and noise robustness compared with the SVM method. In addition, the SRC has an inherent adaptive classification mechanism that makes it suitable for time-varying EEG signal classification for online BCI systems.

Achieving a hybrid brain?computer interface with tactile selective attention and motor imagery

OBJECTIVE We propose a new hybrid brain-computer interface (BCI) system that integrates two different EEG tasks: tactile selective attention (TSA) using a vibro-tactile stimulator on the left/right finger and motor imagery (MI) of left/right hand movement. Event-related desynchronization (ERD) from the MI task and steady-state somatosensory evoked potential (SSSEP) from the TSA task are retrieved and combined into two hybrid senses. APPROACH One hybrid approach is to measure two tasks simultaneously; the features of each task are combined for testing. Another hybrid approach is to measure two tasks consecutively (TSA first and MI next) using only MI features. For comparison with the hybrid approaches, the TSA and MI tasks are measured independently. MAIN RESULTS Using a total of 16 subject datasets, we analyzed the BCI classification performance for MI, TSA and two hybrid approaches in a comparative manner; we found that the consecutive hybrid approach outperformed the others, yielding about a 10% improvement in classification accuracy relative to MI alone. It is understood that TSA may play a crucial role as a prestimulus in that it helps to generate earlier ERD prior to MI and thus sustains ERD longer and to a stronger degree; this ERD may give more discriminative information than ERD in MI alone. SIGNIFICANCE Overall, our proposed consecutive hybrid approach is very promising for the development of advanced BCI systems.

Simple adaptive sparse representation based classification schemes for EEG based brain-computer interface applications

One of the main problems related to electroencephalogram (EEG) based brain-computer interface (BCI) systems is the non-stationarity of the underlying EEG signals. This results in the deterioration of the classification performance during experimental sessions. Therefore, adaptive classification techniques are required for EEG based BCI applications. In this paper, we propose simple adaptive sparse representation based classification (SRC) schemes. Supervised and unsupervised dictionary update techniques for new test data and a dictionary modification method by using the incoherence measure of the training data are investigated. The proposed methods are very simple and additional computation for the re-training of the classifier is not needed. The proposed adaptive SRC schemes are evaluated using two BCI experimental datasets. The proposed methods are assessed by comparing classification results with the conventional SRC and other adaptive classification methods. On the basis of the results, we find that the proposed adaptive schemes show relatively improved classification accuracy as compared to conventional methods without requiring additional computation.

Calibration Time Reduction through Source Imaging in Brain Computer Interface (BCI)

Brain Computer Interface (BCI) is mainly divided into two phases; calibration phase for training and feedback phase. A calibration phase is usually time-consuming, thereby, being likely to raise subjects’ fatigue at the early stage. For more convenient and applicable BCI system it should be investigated to reduce such preparation (calibration) time before feedback phase. Beamformer is a source imaging technique widely used in MEG/EEG source localization problem. It passes only signals produced at the designated source point and filters out other signals such as noise. We conjecture information in source space may be consistent over well trained and good subjects. This idea facilitates to reuse existing datasets from the same or different subjects. Using IVa data in BCI competition III, we constructed a classifier from other 4 subject’s training data and performance was evaluated in source domain. In this work, we observed the proposed approach worked well, resulting in relatively good accuracies (73.21%, 74.21%) for two subjects.

EEG datasets for motor imagery brain–computer interface

Abstract Background: Most investigators of brain–computer interface (BCI) research believe that BCI can be achieved through induced neuronal activity from the cortex, but not by evoked neuronal activity. Motor imagery (MI)–based BCI is one of the standard concepts of BCI, in that the user can generate induced activity by imagining motor movements. However, variations in performance over sessions and subjects are too severe to overcome easily; therefore, a basic understanding and investigation of BCI performance variation is necessary to find critical evidence of performance variation. Here we present not only EEG datasets for MI BCI from 52 subjects, but also the results of a psychological and physiological questionnaire, EMG datasets, the locations of 3D EEG electrodes, and EEGs for non-task-related states. Findings: We validated our EEG datasets by using the percentage of bad trials, event-related desynchronization/synchronization (ERD/ERS) analysis, and classification analysis. After conventional rejection of bad trials, we showed contralateral ERD and ipsilateral ERS in the somatosensory area, which are well-known patterns of MI. Finally, we showed that 73.08% of datasets (38 subjects) included reasonably discriminative information. Conclusions: Our EEG datasets included the information necessary to determine statistical significance; they consisted of well-discriminated datasets (38 subjects) and less-discriminative datasets. These may provide researchers with opportunities to investigate human factors related to MI BCI performance variation, and may also achieve subject-to-subject transfer by using metadata, including a questionnaire, EEG coordinates, and EEGs for non-task-related states.

Increasing session-to-session transfer in a brain-computer interface with on-site background noise acquisition.

OBJECTIVE A brain-computer interface (BCI) usually requires a time-consuming training phase during which data are collected and used to generate a classifier. Because brain signals vary dynamically over time (and even over sessions), this training phase may be necessary each time the BCI system is used, which is impractical. However, the variability in background noise, which is less dependent on a control signal, may dominate the dynamics of brain signals. Therefore, we hypothesized that an understanding of variations in background noise may allow existing data to be reused by incorporating the noise characteristics into the feature extraction framework; in this way, new session data are not required each time and this increases the feasibility of the BCI systems. APPROACH In this work, we collected background noise during a single, brief on-site acquisition session (approximately 3 min) immediately before a new session, and we found that variations in background noise were predictable to some extent. Then we implemented this simple session-to-session transfer strategy with a regularized spatiotemporal filter (RSTF), and we tested it with a total of 20 cross-session datasets collected over multiple days from 12 subjects. We also proposed and tested a bias correction (BC) in the RSTF. MAIN RESULTS We found that our proposed session-to-session strategies yielded a slightly less or comparable performance to the conventional paradigm (each session training phase is needed with an on-site training dataset). Furthermore, using an RSTF only and an RSTF with a BC outperformed existing approaches in session-to-session transfers. SIGNIFICANCE We inferred from our results that, with an on-site background noise suppression feature extractor and pre-existing training data, further training time may be unnecessary.

Cortical Responses and Shape Complexity of Stereoscopic Image - A Simultaneous EEG/MEG Study

Background/Aims: In exploring human factors, stereoscopic 3D images have been used to investigate the neural responses associated with excessive depth, texture complexity, and other factors. However, the cortical oscillation associated with the complexity of stereoscopic images has been studied rarely. Here, we demonstrated that the oscillatory responses to three differently shaped 3D images (circle, star, and bat) increase as the complexity of the image increases. Methods: We recorded simultaneous EEG/MEG for three different stimuli. Spatio-temporal and spatio-spectro-temporal features were investigated by non-parametric permutation test. Results: The results showed that N300 and alpha inhibition increased in the ventral area as the shape complexity of the stereoscopic image increased. Conclusion: It seems that the relative disparity in complex stereoscopic images may increase cognitive processing (N300) and cortical load (alpha inhibition) in the ventral area.

A wellness platform for stereoscopic 3D video systems using EEG-based visual discomfort evaluation technology

Recent advances in three-dimensional (3D) video technology have extended the range of our experience while providing various 3D applications to our everyday life. Nevertheless, the so-called visual discomfort (VD) problem inevitably degrades the quality of experience in stereoscopic 3D (S3D) displays. Meanwhile, electroencephalography (EEG) has been regarded as one of the most promising brain imaging modalities in the field of cognitive neuroscience. In an effort to facilitate comfort with S3D displays, we propose a new wellness platform using EEG. We first reveal features in EEG signals that are applicable to practical S3D video systems as an index for VD perception. We then develop a framework that can automatically determine severe perception of VD based on the EEG features during S3D video viewing by capitalizing on machine-learning-based braincomputer interface technology. The proposed platform can cooperate with advanced S3D video systems whose stereo baseline is adjustable. Thus, the optimal S3D content can be reconstructed according to a viewers sensation of VD. Applications of the proposed platform to various S3D industries are suggested, and further technical challenges are discussed for follow-up research.