Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) stand as state-of-the-art techniques for non-invasive functional neuroimaging. On a unimodal basis, EEG has poor spatial resolution while presenting high temporal resolution. In contrast, fNIRS offers better spatial resolution, though it is constrained by its poor temporal resolution. One important merit shared by the EEG and fNIRS is that both modalities have favorable portability and could be integrated into a compatible experimental setup, providing a compelling ground for the development of a multimodal fNIRS–EEG integration analysis approach. Despite a growing number of studies using concurrent fNIRS-EEG designs reported in recent years, the methodological reference of past studies remains unclear. To fill this knowledge gap, this review critically summarizes the status of analysis methods currently used in concurrent fNIRS–EEG studies, providing an up-to-date overview and guideline for future projects to conduct concurrent fNIRS–EEG studies. A literature search was conducted using PubMed and Web of Science through 31 August 2021. After screening and qualification assessment, 92 studies involving concurrent fNIRS–EEG data recordings and analyses were included in the final methodological review. Specifically, three methodological categories of concurrent fNIRS–EEG data analyses, including EEG-informed fNIRS analyses, fNIRS-informed EEG analyses, and parallel fNIRS–EEG analyses, were identified and explained with detailed description. Finally, we highlighted current challenges and potential directions in concurrent fNIRS–EEG data analyses in future research.

Transcranial direct and alternating current stimulation (tDCS and tACS, respectively) can modulate human brain dynamics and cognition. However, these modalities have not been compared using multiple imaging techniques concurrently. In this study, 15 participants participated in an experiment involving two sessions with a gap of 10 days. In the first and second sessions, tACS and tDCS were administered to the participants. The anode for tDCS was positioned at point FpZ, and four cathodes were positioned over the left and right prefrontal cortices (PFCs) to target the frontal regions simultaneously. tDCS was administered with 1 mA current. tACS was supplied with a current of 1 mA (zero-to-peak value) at 10 Hz frequency. Stimulation was applied concomitantly with functional near-infrared spectroscopy and electroencephalography acquisitions in the resting-state. The statistical test showed significant alteration ( p < 0.001) in the mean hemodynamic responses during and after tDCS and tACS periods. Between-group comparison revealed a significantly less ( p < 0.001) change in the mean hemodynamic response caused by tACS compared with tDCS. As hypothesized, we successfully increased the hemodynamics in both left and right PFCs using tDCS and tACS. Moreover, a significant increase in alpha-band power ( p < 0.01) and low beta band power ( p < 0.05) due to tACS was observed after the stimulation period. Although tDCS is not frequency-specific, it increased but not significantly ( p > 0.05) the powers of most bands including delta, theta, alpha, low beta, high beta, and gamma. These findings suggest that both hemispheres can be targeted and that both tACS and tDCS are equally effective in high-definition configurations, which may be of clinical relevance.

The goal of this study was to develop and validate a hybrid brain-computer interface (BCI) system for home automation control. Over the past decade, BCIs represent a promising possibility in the field of medical (e.g., neuronal rehabilitation), educational, mind reading, and remote communication. However, BCI is still difficult to use in daily life because of the challenges of the unfriendly head device, lower classification accuracy, high cost, and complex operation. In this study, we propose a hybrid BCI system for home automation control with two brain signals acquiring electrodes and simple tasks, which only requires the subject to focus on the stimulus and eye blink. The stimulus is utilized to select commands by generating steady-state visually evoked potential (SSVEP). The single eye blinks (i.e., confirm the selection) and double eye blinks (i.e., deny and re-selection) are employed to calibrate the SSVEP command. Besides that, the short-time Fourier transform and convolution neural network algorithms are utilized for feature extraction and classification, respectively. The results show that the proposed system could provide 38 control commands with a 2 s time window and a good accuracy (i.e., 96.92%) using one bipolar electroencephalogram (EEG) channel. This work presents a novel BCI approach for the home automation application based on SSVEP and eye blink signals, which could be useful for the disabled. In addition, the provided strategy of this study—a friendly channel configuration (i.e., one bipolar EEG channel), high accuracy, multiple commands, and short response time—might also offer a reference for the other BCI controlled applications.

This paper aims to develop a Brain-computer interface (BCI) speller utilizing eyes-closed and double-blinking electroencephalogram (EEG) signals and based on asynchronous mechanism. The proposed system comprises a signal processing module and a graphical user interface (virtual keyboard-VK) with 26 English characters plus a special symbol. A detected “eyes-closed”(EC) event induces the “select”command, whereas a “double-blinking”(DB) event functions as the “undo”command. A three-class support vector machine classifier involving EEG signal analysis of three groups of events (“eyes-open” - idle state, EC, and DB) is proposed. The results show that the proposed BCI can achieve an overall accuracy of 93.8% for multi-class classification. The proposed speller is then employed in the online experiment of spelling the word “bcispeller”using a 1 s time window. Consequently, it achieves an average accuracy of 92.3% and an average spelling rate of 5 letters/min. Overall, this paper shows improvement with high accuracy and spelling rate demonstrating the feasibility and reliability of implementing a real-world BCI speller.

Transcranial electrical stimulation has demonstrated the potential to enhance cognitive functions such as working memory, learning capacity, and attentional allocation. Recently, it was shown that periodic stimulation within a specific duration could augment the human brain’s neuroplasticity. This study investigates the effects of repetitive transcranial alternating current stimulation (tACS; 1 mA, 5 Hz, 2 min duration) on cognitive function, functional connectivity, and topographic changes using both electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). Fifteen healthy subjects were recruited to measure brain activity in the pre-, during-, and post-stimulation sessions under tACS and sham stimulation conditions. Fourteen trials of working memory tasks and eight repetitions of tACS/sham stimulation with a 1-minute intersession interval were applied to the frontal cortex of the participants. The working memory score, EEG band-wise powers, EEG topography, concentration changes of oxygenated hemoglobin, and functional connectivity (FC) were individually analyzed to quantify the behavioral and neurophysiological effects of tACS. Our results indicate that tACS increases: i) behavioral scores (i.e., 15.08, p <0.001) and EEG band-wise powers (i.e., theta and beta bands) compared to the sham stimulation condition, ii) FC of both EEG-fNIRS signals, especially in the large-scale brain network communication and interhemispheric connections, and iii) the hemodynamic response in comparison to the pre-stimulation session and the sham condition. Conclusively, the repetitive theta-band tACS stimulation improves the working memory capacity regarding behavioral and neuroplasticity perspectives. Additionally, the proposed fNIRS biomarkers (mean, slope), EEG band-wise powers, and FC can be used as neuro-feedback indices for closed-loop brain stimulation.