Neuromodulatory Effects of HD-tACS/tDCS on the Prefrontal Cortex: A Resting-State fNIRS-EEG Study

Ghafoor, Usman; Yang, Dalin; Hong, Keum‐Shik

doi:10.1109/jbhi.2021.3127080

Cited by 29 publications

(12 citation statements)

References 105 publications

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“…However, unlike EEG, fNIRS provides better spatial resolution. Moreover, fNIRS is less influenced by electrical noise (Hasan et al, 2020 ; Ghafoor et al, 2022 ). As evidenced by the comparison, fNIRS can compensate for the trade-offs of EEG.…”

Section: Introductionmentioning

confidence: 99%

EEG-fNIRS-based hybrid image construction and classification using CNN-LSTM

et al. 2022

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The constantly evolving human–machine interaction and advancement in sociotechnical systems have made it essential to analyze vital human factors such as mental workload, vigilance, fatigue, and stress by monitoring brain states for optimum performance and human safety. Similarly, brain signals have become paramount for rehabilitation and assistive purposes in fields such as brain–computer interface (BCI) and closed-loop neuromodulation for neurological disorders and motor disabilities. The complexity, non-stationary nature, and low signal-to-noise ratio of brain signals pose significant challenges for researchers to design robust and reliable BCI systems to accurately detect meaningful changes in brain states outside the laboratory environment. Different neuroimaging modalities are used in hybrid settings to enhance accuracy, increase control commands, and decrease the time required for brain activity detection. Functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) measure the hemodynamic and electrical activity of the brain with a good spatial and temporal resolution, respectively. However, in hybrid settings, where both modalities enhance the output performance of BCI, their data compatibility due to the huge discrepancy between their sampling rate and the number of channels remains a challenge for real-time BCI applications. Traditional methods, such as downsampling and channel selection, result in important information loss while making both modalities compatible. In this study, we present a novel recurrence plot (RP)-based time-distributed convolutional neural network and long short-term memory (CNN-LSTM) algorithm for the integrated classification of fNIRS EEG for hybrid BCI applications. The acquired brain signals are first projected into a non-linear dimension with RPs and fed into the CNN to extract essential features without performing any downsampling. Then, LSTM is used to learn the chronological features and time-dependence relation to detect brain activity. The average accuracies achieved with the proposed model were 78.44% for fNIRS, 86.24% for EEG, and 88.41% for hybrid EEG-fNIRS BCI. Moreover, the maximum accuracies achieved were 85.9, 88.1, and 92.4%, respectively. The results confirm the viability of the RP-based deep-learning algorithm for successful BCI systems.

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Section: Introductionmentioning

confidence: 99%

EEG-fNIRS-based hybrid image construction and classification using CNN-LSTM

et al. 2022

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“…Few studies looked at training effects of different stimulation protocols [57,173], or their ideal onset of stimulation with respect to training sessions [72]; however, no comparison of transfer effects has been conducted, except for studies looking at cognitive/arithmetical abilities [173] or targeting higher-level cortical regions [174]. Crucially, transfer of learning is a more relevant measure of the translational value of a technique when it comes to its rehabilitative application.…”

Section: Open Questionsmentioning

confidence: 99%

Perspectives on the Combined Use of Electric Brain Stimulation and Perceptual Learning in Vision

Maniglia

2022

Vision

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A growing body of literature offers exciting perspectives on the use of brain stimulation to boost training-related perceptual improvements in humans. Recent studies suggest that combining visual perceptual learning (VPL) training with concomitant transcranial electric stimulation (tES) leads to learning rate and generalization effects larger than each technique used individually. Both VPL and tES have been used to induce neural plasticity in brain regions involved in visual perception, leading to long-lasting visual function improvements. Despite being more than a century old, only recently have these techniques been combined in the same paradigm to further improve visual performance in humans. Nonetheless, promising evidence in healthy participants and in clinical population suggests that the best could still be yet to come for the combined use of VPL and tES. In the first part of this perspective piece, we briefly discuss the history, the characteristics, the results and the possible mechanisms behind each technique and their combined effect. In the second part, we discuss relevant aspects concerning the use of these techniques and propose a perspective concerning the combined use of electric brain stimulation and perceptual learning in the visual system, closing with some open questions on the topic.

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“…An age-and wavelength-dependent differential pathlength factor was calculated for each subject [31]. In this study, the HbR time course showed insignificant changes in comparison to the HbO time course throughout the experiment [32,33]; therefore, we only focused on HbO concentration changes. fNIRS 8 signals were filtered using a Butterworth low-pass filter (4 th order) with a cut-off frequency of 0.1 Hz [32].…”

Section: Preprocessing and Data Analysismentioning

confidence: 99%

Investigation of neuromodulatory effect of anodal cerebellar transcranial direct current stimulation on the primary motor cortex using functional near-infrared spectroscopy

Shoaib

Chang

Lee

et al. 2022

Preprint

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Background: Cerebellar brain inhibition (CBI), a neural connection between the cerebellum and primary motor cortex (M1), has been researched as a target pathway for neuromodulation to improve clinical outcomes in various neurological diseases. However, conflicting results of anodal cerebellar transcranial direct current stimulation (acb-tDCS) on M1 excitability indicate that additional investigation is required to examine its precise effect. Objective/Hypothesis: This study aimed to gather evidence of the neuromodulatory effect of acb-tDCS on the M1 using functional near-infrared spectroscopy (fNIRS). Methods: Sixteen healthy participants were included in this cross-over study. Participants received real and sham acb-tDCS in a random order, with a minimum one-week washout period between them. The anode and cathode were placed on the right cerebellum and the right buccinator muscle, respectively. Stimulation lasted 20 min at an intensity of 2 mA, and fNIRS data were recorded for 42 min (including a 4 min baseline before stimulation and an 18 min post-stimulation duration) using eight channels attached bilaterally on the M1. Results: acb-tDCS induced a significant decrease in oxyhemoglobin (HbO) concentration (inhibitory effect) in the left (contralateral) M1, whereas it induced a significant increase in HbO concentration (excitatory effect) in the right (ipsilateral) M1 compared to sham tDCS during (p < 0.05) and after stimulation (p < 0.01) in a group level analysis. At the individual level, variations in the response to acb-tDCS were observed. Conclusion: Our findings demonstrate the neuromodulatory effects of acb-tDCS on the bilateral M1 in terms of neuronal hemodynamics.

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Neuromodulatory Effects of HD-tACS/tDCS on the Prefrontal Cortex: A Resting-State fNIRS-EEG Study

Cited by 29 publications

References 105 publications

EEG-fNIRS-based hybrid image construction and classification using CNN-LSTM

EEG-fNIRS-based hybrid image construction and classification using CNN-LSTM

Perspectives on the Combined Use of Electric Brain Stimulation and Perceptual Learning in Vision

Investigation of neuromodulatory effect of anodal cerebellar transcranial direct current stimulation on the primary motor cortex using functional near-infrared spectroscopy

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