Brain Computer Interfacing: A Multi-Modal Perspective

Fazli, Siamac; Lee, Seong Whan

doi:10.5626/jcse.2013.7.2.132

Cited by 19 publications

(4 citation statements)

References 21 publications

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“…This work presents some evidence, that EEG assisted learning may be a viable application for the future. Multimodal neuroimaging [18], [19], a more thorough understanding of the mental processes involved as well as more advanced machine methods may be necessary improve on the decoding of mental states, such as attention, mental workload, working memory and others during the process of learning. If these goals can be met and combined with dry electrode technology [20], neuralassisted learning may become common practice in the future.…”

Section: Discussionmentioning

confidence: 99%

Single-trials ERPs predict correct answers to intelligence test questions

Leydecker

Bieβmann²,

Fazli

2014

2014 International Workshop on Pattern Recognition in Neuroimaging

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Neurotechnology offers the potential to improve performance in cognitive tasks by tailoring the learning paradigm to the neurophysiological correlates of mental processes. Up to date, there are few studies that investigate the single trial performance of neural decoding in cognitive tasks. In this study we examine EEG data while a given subject is solving questions which are commonly used in intelligence quotient tests. Subjects are instructed to solve a number of visual template matching tasks. Our findings suggest that it is possible to decode the true answer from the subjects' ERP responses at the time of its presentation. These results indicate that neurophysiological markers could be useful for neurotechnology assisted learning paradigms.

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Section: Discussionmentioning

confidence: 99%

Single-trials ERPs predict correct answers to intelligence test questions

Leydecker

Bieβmann²,

Fazli

2014

2014 International Workshop on Pattern Recognition in Neuroimaging

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“…EEG-NIRS hybrids have proven their merit in a variety of research fields 19–23 . For example, hybrid EEG-NIRS BCI systems showed better classification accuracy than each unimodal BCI system 26 , 27 , and EEG-NIRS hybrids were also used to better understand the linguistic functions of newborns and infants 28 , 29 . EEG-NIRS correlation analyses helped further to reveal the intricate relationship between electrophysiological and hemodynamic changes in neuroscience 30 .…”

Section: Background and Summarymentioning

confidence: 99%

Simultaneous acquisition of EEG and NIRS during cognitive tasks for an open access dataset

et al. 2018

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We provide an open access multimodal brain-imaging dataset of simultaneous electroencephalography (EEG) and near-infrared spectroscopy (NIRS) recordings. Twenty-six healthy participants performed three cognitive tasks: 1) n-back (0-, 2- and 3-back), 2) discrimination/selection response task (DSR) and 3) word generation (WG) tasks. The data provided includes: 1) measured data, 2) demographic data, and 3) basic analysis results. For n-back (dataset A) and DSR tasks (dataset B), event-related potential (ERP) analysis was performed, and spatiotemporal characteristics and classification results for ‘target’ versus ‘non-target’ (dataset A) and symbol ‘O’ versus symbol ‘X’ (dataset B) are provided. Time-frequency analysis was performed to show the EEG spectral power to differentiate the task-relevant activations. Spatiotemporal characteristics of hemodynamic responses are also shown. For the WG task (dataset C), the EEG spectral power and spatiotemporal characteristics of hemodynamic responses are analyzed, and the potential merit of hybrid EEG-NIRS BCIs was validated with respect to classification accuracy. We expect that the dataset provided will facilitate performance evaluation and comparison of many neuroimaging analysis techniques.

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“…The NIRS is relatively robust to electrical noise [ 21 , 22 , 23 , 24 , 25 ], and thus, the disadvantage of a conventional EEG-BCI is compensated if both EEG and NIRS are combined for developing BCIs. The most important advantage obtained when NIRS is combined with EEG is that the information not included in the EEG is obtained because the NIRS hemodynamic response is a physiological signal induced by a mechanism different from that of the EEG [ 26 ]. Hence, combining EEG and NIRS are well suited for the hybrid BCI as their advantages and disadvantages are complementary.…”

Section: Introductionmentioning

confidence: 99%

Improvement of Information Transfer Rates Using a Hybrid EEG-NIRS Brain-Computer Interface with a Short Trial Length: Offline and Pseudo-Online Analyses

Shin

Kim

Müller

et al. 2018

Sensors

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Electroencephalography (EEG) and near-infrared spectroscopy (NIRS) are non-invasive neuroimaging methods that record the electrical and metabolic activity of the brain, respectively. Hybrid EEG-NIRS brain-computer interfaces (hBCIs) that use complementary EEG and NIRS information to enhance BCI performance have recently emerged to overcome the limitations of existing unimodal BCIs, such as vulnerability to motion artifacts for EEG-BCI or low temporal resolution for NIRS-BCI. However, with respect to NIRS-BCI, in order to fully induce a task-related brain activation, a relatively long trial length (≥10 s) is selected owing to the inherent hemodynamic delay that lowers the information transfer rate (ITR; bits/min). To alleviate the ITR degradation, we propose a more practical hBCI operated by intuitive mental tasks, such as mental arithmetic (MA) and word chain (WC) tasks, performed within a short trial length (5 s). In addition, the suitability of the WC as a BCI task was assessed, which has so far rarely been used in the BCI field. In this experiment, EEG and NIRS data were simultaneously recorded while participants performed MA and WC tasks without preliminary training and remained relaxed (baseline; BL). Each task was performed for 5 s, which was a shorter time than previous hBCI studies. Subsequently, a classification was performed to discriminate MA-related or WC-related brain activations from BL-related activations. By using hBCI in the offline/pseudo-online analyses, average classification accuracies of 90.0 ± 7.1/85.5 ± 8.1% and 85.8 ± 8.6/79.5 ± 13.4% for MA vs. BL and WC vs. BL, respectively, were achieved. These were significantly higher than those of the unimodal EEG- or NIRS-BCI in most cases. Given the short trial length and improved classification accuracy, the average ITRs were improved by more than 96.6% for MA vs. BL and 87.1% for WC vs. BL, respectively, compared to those reported in previous studies. The suitability of implementing a more practical hBCI based on intuitive mental tasks without preliminary training and with a shorter trial length was validated when compared to previous studies.

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Brain Computer Interfacing: A Multi-Modal Perspective

Cited by 19 publications

References 21 publications

Single-trials ERPs predict correct answers to intelligence test questions

Single-trials ERPs predict correct answers to intelligence test questions

Simultaneous acquisition of EEG and NIRS during cognitive tasks for an open access dataset

Improvement of Information Transfer Rates Using a Hybrid EEG-NIRS Brain-Computer Interface with a Short Trial Length: Offline and Pseudo-Online Analyses

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