A Brain–Computer Interface-Based Vehicle Destination Selection System Using P300 and SSVEP Signals

Khan

2018

Front. Hum. Neurosci.

199

149

In this study, a brain-computer interface (BCI) framework for hybrid functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) for locked-in syndrome (LIS) patients is investigated. Brain tasks, channel selection methods, and feature extraction and classification algorithms available in the literature are reviewed. First, we categorize various types of patients with cognitive and motor impairments to assess the suitability of BCI for each of them. The prefrontal cortex is identified as a suitable brain region for imaging. Second, the brain activity that contributes to the generation of hemodynamic signals is reviewed. Mental arithmetic and word formation tasks are found to be suitable for use with LIS patients. Third, since a specific targeted brain region is needed for BCI, methods for determining the region of interest are reviewed. The combination of a bundled-optode configuration and threshold-integrated vector phase analysis turns out to be a promising solution. Fourth, the usable fNIRS features and EEG features are reviewed. For hybrid BCI, a combination of the signal peak and mean fNIRS signals and the highest band powers of EEG signals is promising. For classification, linear discriminant analysis has been most widely used. However, further research on vector phase analysis as a classifier for multiple commands is desirable. Overall, proper brain region identification and proper selection of features will improve classification accuracy. In conclusion, five future research issues are identified, and a new BCI scheme, including brain therapy for LIS patients and using the framework of hybrid fNIRS-EEG BCI, is provided.

Section: Introductionmentioning

confidence: 99%

Feature Extraction and Classification Methods for Hybrid fNIRS-EEG Brain-Computer Interfaces

Khan

2018

Front. Hum. Neurosci.

199

149

“…Hence 4-D paradigm can be useful in developing new BCI applications. Fan et al (2015) developed a new driver-vehicle interface for severely disabled persons by using P300 and SSVEP BCIs to perform a destination selection. The P300 visual stimuli displayed on an LCD monitor was a 3*3 matrix of characters.…”

Section: P300/ssvep Based Bcimentioning

confidence: 99%

The types of hybrid modalities in brain-computer interface systems: a review

Singla

Jatana

2017

IJBET

Brain-Computer Interface (BCI) is a young research area for researchers. Increasing number of research activities improves several areas such as signal acquisition techniques, hardware development, machine learning, and signal processing and system integration. However, there are many disadvantages of conventional BCI approaches. For example, Motorimagery based BCIs requires extensive training of the subjects, P-300 based BCIs still requires several stimulus repetitions to obtain reliable accuracy and in SSVEP stimulus; number of commands is limited by the number of stimulus frequencies and many more. To overcome these disadvantages and further improvement in performance of the system, an increasing number of researchers have begun to explore hybrid BCI approaches, in which multiple BCI approaches are incorporated in a single BCI system. The purpose of this paper is to give a brief introduction to the different types of hybrid BCI techniques. There are many different types of hybrid BCI that can be used in a wide range of applications. The paradigm design plays a very important role in the performance of hybrid BCIs.

Phil. Trans. R. Soc. A.

“…In order to evaluate the performance of the proposed detection algorithm, it is essential to have prior knowledge of whether P300 of a subject was present after each target in the recorded data. To this end, we propose the following three criteria to determine the presence of P300: (i) at least one positive peak must be present, (ii) a negative trend must not exist, and (iii) if two or more positive peaks do exist, the difference in the magnitude between the largest and second-largest peaks (d 1 ) must be higher than half of the difference in the magnitude between the largest peak and the 'valley' located between the largest and second-largest peaks (d 2 Figure 8a shows an EEG signal with P300 where: (1) at least one positive peak is present, (2) no negative trend exists, and (3) 1st peak -2nd peak) was larger than half the difference between the first peak and the valley ( Figure 8b shows an EEG signal without P300 where:…”

Section: Applications Of Intrinsic Multiscale Analysis To Cooperativementioning

confidence: 99%

Adaptive-projection intrinsically transformed multivariate empirical mode decomposition in cooperative brain–computer interface applications

Hemakom¹,

Goverdovsky²,

Looney³

et al. 2016

One contribution of 13 to a theme issue 'Adaptive data analysis: theory and applications' . An extension to multivariate empirical mode decomposition (MEMD), termed adaptive-projection intrinsically transformed MEMD (APIT-MEMD), is proposed to cater for power imbalances and inter-channel correlations in real-world multichannel data. It is shown that the APIT-MEMD exhibits similar or better performance than MEMD for a large number of projection vectors, whereas it outperforms MEMD for the critical case of a small number of projection vectors within the sifting algorithm. We also employ the noise-assisted APIT-MEMD within our proposed intrinsic multiscale analysis framework and illustrate the advantages of such an approach in notoriously noise-dominated cooperative braincomputer interface (BCI) based on the steady-state visual evoked potentials and the P300 responses. Finally, we show that for a joint cognitive BCI task, the proposed intrinsic multiscale analysis framework improves system performance in terms of the information transfer rate.