Neural dynamics of delayed feedback in robot teleoperation: insights from fNIRS analysis.

Introduction: As robot teleoperation increasingly becomes integral in executing tasks in distant, hazardous, or inaccessible environments, operational delays remain a significant obstacle. These delays, inherent in signal transmission and processing, adversely affect operator performance, particularly in tasks requiring precision and timeliness. While current research has made strides in mitigating these delays through advanced control strategies and training methods, a crucial gap persists in understanding the neurofunctional impacts of these delays and the efficacy of countermeasures from a cognitive perspective. Methods: This study addresses the gap by leveraging functional Near-Infrared Spectroscopy (fNIRS) to examine the neurofunctional implications of simulated haptic feedback on cognitive activity and motor coordination under delayed conditions. In a human-subject experiment (N = 41), sensory feedback was manipulated to observe its influences on various brain regions of interest (ROIs) during teleoperation tasks. The fNIRS data provided a detailed assessment of cerebral activity, particularly in ROIs implicated in time perception and the execution of precise movements. Results: Our results reveal that the anchoring condition, which provided immediate simulated haptic feedback with a delayed visual cue, significantly optimized neural functions related to time perception and motor coordination. This condition also improved motor performance compared to the asynchronous condition, where visual and haptic feedback were misaligned. Discussion: These findings provide empirical evidence about the neurofunctional basis of the enhanced motor performance with simulated synthetic force feedback in the presence of teleoperation delays. The study highlights the potential for immediate haptic feedback to mitigate the adverse effects of operational delays, thereby improving the efficacy of teleoperation in critical applications.

The characteristics of WM in individuals with depressive tendencies: A functional near-infrared spectroscopy (fNIRS) study.

Individuals with depressive tendencies are considered to be at high risk for the onset of depressive disorders. Currently, most research focuses on the impairment of working memory in patients with depression, while there is less attention paid to the WM of individuals with depressive tendencies, and their neural mechanisms underlying it are poorly understood. Therefore, this study focuses on the characteristics and neural mechanisms of WM in individuals with depressive tendencies. This study uses functional near-infrared spectroscopy (fNIRS) to monitor the concentration of Oxy-Hb in the prefrontal cortex and employs the n-back paradigm, designing three levels of load: 0, 1, and 2, to examine the characteristics of WM and its neural mechanisms in individuals with depressive tendencies. Behavioral results show that the accuracy rates of individuals with depressive tendencies is significantly lower than that of healthy individuals, and under the 0-back condition, the reaction time of individuals with depressive tendencies is significantly higher than that of healthy control individuals. Near-infrared results indicate that the activation level in the frontal pole and the dorsal lateral prefrontal cortex of individuals with depressive tendencies is significantly lower than that of healthy control individuals. The ß values of channels 2, 7, and 9 are significantly negatively correlated with the Beck Depression Inventory scores of the participants. The results suggest that the reduced activation of the frontal pole and dorsal lateral prefrontal cortex in individuals with depressive tendencies leads to poorer WM performance compared to healthy control individuals. This is a rare brain evidence of the characteristics of WM in individuals with depressive tendencies, which can provide a deeper understanding of the WM characteristics of individuals with depressive tendencies.

Cardiac vagal activity changes moderated the association of cognitive and cerebral hemodynamic variations in the prefrontal cortex.

Phasic cardiac vagal activity (CVA), reflecting ongoing, moment-to-moment psychophysiological adaptations to environmental changes, can serve as a predictor of individual difference in executive function, particularly executive performance. However, the relationship between phasic CVA and executive function demands requires further validation because of previous inconsistent findings. Moreover, it remains unclear what types of phasic changes of CVA may be adaptive in response to heightened executive demands. This study used the standard N-back task to induce different levels of working memory (WM) load and combined functional Near-Infrared Spectroscopy (fNIRS) with a multipurpose polygraph to investigate the variations of CVA and its interactions with cognitive and prefrontal responses as executive demands increased in fifty-two healthy young subjects. Our results showed phasic decreases in CVA as WM load increased (t (51) = -3.758, p < 0.001, Cohen's d = 0.526). Furthermore, phasic changes of CVA elicited by increased executive demands moderated the association of cognitive and cerebral hemodynamic variations in the prefrontal cortex (B = 0.038, SE = 0.014, p < 0.05). Specifically, as executive demands increased, individuals with larger phasic CVA withdrawal showed a positive relationship between cognitive and hemodynamic variations in the prefrontal cortex (ß = 0.281, p = 0.031). No such significant relationship was observed in individuals with smaller phasic CVA withdrawal. The current findings demonstrate a decrease in CVA with increasing executive demands and provide empirical support for the notion that a larger phasic CVA withdrawal can be considered adaptive in situations requiring high executive function demands.

Neuroplasticity of Speech-in-Noise Processing in Older Adults Assessed by Functional Near-Infrared Spectroscopy (fNIRS).

Functional near-infrared spectroscopy (fNIRS), a non-invasive optical neuroimaging technique that is portable and acoustically silent, has become a promising tool for evaluating auditory brain functions in hearing-vulnerable individuals. This study, for the first time, used fNIRS to evaluate neuroplasticity of speech-in-noise processing in older adults. Ten older adults, most of whom had moderate-to-mild hearing loss, participated in a 4-week speech-in-noise training. Their speech-in-noise performances and fNIRS brain responses to speech (auditory sentences in noise), non-speech (spectrally-rotated speech in noise) and visual (flashing chequerboards) stimuli were evaluated pre- (T0) and post-training (immediately after training, T1; and after a 4-week retention, T2). Behaviourally, speech-in-noise performances were improved after retention (T2 vs. T0) but not immediately after training (T1 vs. T0). Neurally, we intriguingly found brain responses to speech vs. non-speech decreased significantly in the left auditory cortex after retention (T2 vs. T0 and T2 vs. T1) for which we interpret as suppressed processing of background noise during speech listening alongside the significant behavioural improvements. Meanwhile, functional connectivity within and between multiple regions of temporal, parietal and frontal lobes was significantly enhanced in the speech condition after retention (T2 vs. T0). We also found neural changes before the emergence of significant behavioural improvements. Compared to pre-training, responses to speech vs. non-speech in the left frontal/prefrontal cortex were decreased significantly both immediately after training (T1 vs. T0) and retention (T2 vs. T0), reflecting possible alleviation of listening efforts. Finally, connectivity was significantly decreased between auditory and higher-level non-auditory (parietal and frontal) cortices in response to visual stimuli immediately after training (T1 vs. T0), indicating decreased cross-modal takeover of speech-related regions during visual processing. The results thus showed that neuroplasticity can be observed not only at the same time with, but also before, behavioural changes in speech-in-noise perception. To our knowledge, this is the first fNIRS study to evaluate speech-based auditory neuroplasticity in older adults. It thus provides important implications for current research by illustrating the promises of detecting neuroplasticity using fNIRS in hearing-vulnerable individuals.

Hemodynamic activity is not parsimoniously tuned to index-of-difficulty in movement with dual requirements on speed-accuracy.

Background: Reaching movements are crucial for daily living and rehabilitation, for which Fitts' Law describes a speed-accuracy trade-off that movement time increases with task difficulty. This study aims to investigate whether cortical activation in motor-related areas is directly linked to task difficulty as defined by Fitts' Law. Understanding this relationship provides a physiological basis for parameter selection in therapeutic exercises. Methods: Sixteen healthy subjects performed 2D reaching movements using a rehabilitation robot, with their cortical responses detected using functional near-infrared spectroscopy (fNIRS). Task difficulty was manipulated by varying target size and distance, resulting in 3 levels of index-of-difficulty (ID). Kinematic signals were recorded alongside cortical activity to assess the relationship among movement time, task difficulty, and cortical activation. Results: Our results showed that movement time increased with ID by 0.2974s/bit across all subjects (conditional r2 = 0.6434, p < 0.0001), and all subjects showed individual trends conforming Fitts' Law (all p < 0.001). Neither activation in BA4 nor in BA6 showed a significant correlation with ID (p > 0.05), while both the target size and distance, as well as the interaction between them, showed a significant relationship with BA4 or BA6 activation (all p < 0.05). Conclusion: This study found that although kinematic measures supported Fitts' Law, cortical activity in motor-related areas during reaching movements did not correlate directly with task difficulty as defined by Fitts' Law. Additional factors such as muscle activation may call for different cortical control even when difficulty was identical.

Teach a man to fish: Hyper-brain evidence on scaffolding strategy enhancing creativity acquisition and transfer.

Creativity is an indispensable competency in today's innovation-driven society. Yet, the influences of instructional strategy, a key determinant of educational outcomes, on the creativity-fostering process remains an unresolved mystery. We proposed that instructional strategy affects creativity cultivation and further investigated the intricate neural mechanisms underlying this relationship. In a naturalistic laboratory setting, 66 instructor-learner dyads were randomized into three groups (scaffolding, explanation, and control), with divergent thinking instructions separately. Functional near-infrared spectroscopy (fNIRS) hyperscanning simultaneously collected brain signals in the prefrontal cortex and temporal-parietal junction regions. Results indicated that learners instructed with a scaffolding strategy demonstrated superior creative performance both in acquisition (direct learning) and transfer (use in a novel context) of creativity skills, compared to pretest levels. In contrast, the control and explanation groups did not exhibit such effects. Notably, we also observed remarkable interbrain neural synchronization (INS) between instructors and learners in the left superior frontal cortex in the scaffolding group, but not in the explanation or control groups. Furthermore, INS positively predicted enhancements in creativity performance (acquisition and transfer), indicating that it is a crucial neural mechanism in the creativity-fostering process. These findings reveal that scaffolding facilitates the acquisition and transfer of creativity and deepen our understanding of the neural mechanisms underlying the process of creativity-fostering. The current study provides valuable insights for implementing teaching strategies to fostering creativity.

Speaker-listener neural coupling correlates with semantic and acoustic features of naturalistic speech.

Recent research has extensively reported the phenomenon of inter-brain neural coupling between speakers and listeners during speech communication. Yet, the specific speech processes underlying this neural coupling remain elusive. To bridge this gap, this study estimated the correlation between the temporal dynamics of speaker-listener neural coupling with speech features, utilizing two inter-brain datasets accounting for different noise levels and listener's language experiences (native vs. non-native). We first derived time-varying speaker-listener neural coupling, extracted acoustic feature (envelope) and semantic features (entropy and surprisal) from speech, and then explored their correlational relationship. Our findings reveal that in clear conditions, speaker-listener neural coupling correlates with semantic features. However, as noise increases, this correlation is only significant for native listeners. For non-native listeners, neural coupling correlates predominantly with acoustic feature rather than semantic features. These results revealed how speaker-listener neural coupling is associated with the acoustic and semantic features under various scenarios, enriching our understanding of the inter-brain neural mechanisms during natural speech communication. We therefore advocate for more attention on the dynamic nature of speaker-listener neural coupling and its modeling with multilevel speech features.

Online chasing action recruits both mirror neuron and mentalizing systems: A pilot fNIRS study.

Engaging in chasing, where an actor actively pursues a target, is considered a crucial activity for the development of social skills. Previous studies have focused predominantly on understanding the neural correlates of chasing from an observer's perspective, but the neural mechanisms underlying the real-time implementation of chasing action remain poorly understood. To gain deeper insights into this phenomenon, the current study employed functional near-infrared spectroscopy (fNIRS) techniques and a novel interactive game. In this interactive game, participants (N = 29) were tasked to engage in chasing behavior by controlling an on-screen character using a gamepad, with the goal of catching a virtual partner. To specifically examine the brain activations associated with the interactive nature of chasing, we included two additional interactive actions: following action of following the path of a virtual partner and free action of moving without a specific pursuit goal. The results revealed that chasing and following actions elicited activation in a broad and overlapping network of brain regions, including the temporoparietal junction (TPJ), medial prefrontal cortex (mPFC), premotor cortex (PMC), primary somatosensory cortex (SI), and primary motor cortex (M1). Crucially, these regions were found to be modulated by the type of interaction, with greater activation and functional connectivity during the chasing interaction than during the following and free interactions. These findings suggested that both the MNS, encompassing regions such as the PMC, M1 and SI, and the mentalizing system (MS), involving the TPJ and mPFC, contribute to the execution of online chasing actions. Thus, the present study represents an initial step toward future investigations into the roles of MNS and MS in real-time chasing interactions.

EEG and fNIRS are associated with situation awareness (hazard) prediction during a driving task.

Situation awareness (SA) is important in many demanding tasks (e.g. driving). Assessing SA during training can indicate whether someone is ready to perform in the real world. SA is typically assessed by interrupting the task to ask questions about the situation or asking questions after task completion, assessing only momentary SA. An objective and continuous means of detecting SA is needed. We examined whether neurophysiological sensors are useful to objectively measure Level 3 SA (projection of events into the future) during a driving task. We measured SA by the speed at which participants responded to SA questions and the accuracy of responses. For EEG, beta and theta power were most sensitive to SA response time. For fNIRS, oxygenated haemoglobin (HbO) was most sensitive to accuracy. This is the first evidence to our knowledge that neurophysiological measures are useful for assessing Level 3 SA during an ecologically valid task.

We examine whether neurophysiological sensors are useful to objectively measure Level 3 situation awareness (SA) prediction during a driving task. EEG theta and beta, and fNIRS oxygenated haemoglobin were most sensitive to SA accuracy. This is evidence that neurophysiological measures can be used to assess hazard prediction (Level 3 SA).

Bringing together multimodal and multilevel approaches to study the emergence of social bonds between children and improve social AI.

This protocol paper outlines an innovative multimodal and multilevel approach to studying the emergence and evolution of how children build social bonds with their peers, and its potential application to improving social artificial intelligence (AI). We detail a unique hyperscanning experimental framework utilizing functional near-infrared spectroscopy (fNIRS) to observe inter-brain synchrony in child dyads during collaborative tasks and social interactions. Our proposed longitudinal study spans middle childhood, aiming to capture the dynamic development of social connections and cognitive engagement in naturalistic settings. To do so we bring together four kinds of data: the multimodal conversational behaviors that dyads of children engage in, evidence of their state of interpersonal rapport, collaborative performance on educational tasks, and inter-brain synchrony. Preliminary pilot data provide foundational support for our approach, indicating promising directions for identifying neural patterns associated with productive social interactions. The planned research will explore the neural correlates of social bond formation, informing the creation of a virtual peer learning partner in the field of Social Neuroergonomics. This protocol promises significant contributions to understanding the neural basis of social connectivity in children, while also offering a blueprint for designing empathetic and effective social AI tools, particularly for educational contexts.

Impact of repetitive transcranial magnetic stimulation on cortical activity: a systematic review and meta-analysis utilizing functional near-infrared spectroscopy evaluation.

BACKGROUND: Repeated transcranial magnetic stimulation (rTMS) could induce alterations in cortical excitability and promote neuroplasticity. To precisely quantify these effects, functional near-infrared spectroscopy (fNIRS), an optical neuroimaging modality adept at detecting changes in cortical hemodynamic responses, has been employed concurrently alongside rTMS to measure and tailor the impact of diverse rTMS protocols on the brain cortex. OBJECTIVE: This systematic review and meta-analysis aimed to elucidate the effects of rTMS on cortical hemodynamic responses over the primary motor cortex (M1) as detected by fNIRS. METHODS: Original articles that utilized rTMS to stimulate the M1 cortex in combination with fNIRS for the assessment of cortical activity were systematically searched across the PubMed, Embase, and Scopus databases. The search encompassed records from the inception of these databases up until April, 2024. The assessment for risk of bias was also conducted. A meta-analysis was also conducted in studies with extractable raw data. RESULTS: Among 312 studies, 14 articles were eligible for qualitative review. 7 studies were eligible for meta-analysis. A variety of rTMS protocols was employed on M1 cortex. In inhibitory rTMS, multiple studies observed a reduction in the concentration of oxygenated hemoglobin [HbO] at the ipsilateral M1, contrasted by an elevation at the contralateral M1. Meta-analysis also corroborated this consistent trend. Nevertheless, certain investigations unveiled diminished [HbO] in bilateral M1. Several studies also depicted intricate inhibitory or excitatory interplay among distinct cortical regions. CONCLUSION: Diverse rTMS protocols led to varied patterns of cortical activity detected by fNIRS. Meta-analysis revealed a trend of increasing [HbO] in the contralateral cortices and decreasing [HbO] in the ipsilateral cortices following low frequency inhibitory rTMS. However, due to the heterogeneity between studies, further research is necessary to comprehensively understand rTMS-induced alterations in brain activity.

Intergenerational or intragenerational learning? The relationship between interpersonal neural synchrony and older adult's learning acquisition.

OBJECTIVES: Lifelong learning facilitates active ageing, and intragenerational learning-the process by which older adults learn from their peers-is an effective means of achieving this goal. The present research aims to elucidate the mechanisms and differences between intergenerational and intragenerational learning models for older adults as evidenced by brain-to-brain synchrony. METHODS: Fifty-six instructor-learner dyads completed a study comparing intergenerational and intragenerational learning models, as well as task difficulty. The study utilized a block puzzle task and functional near-infrared spectroscopy (fNIRS) for hyperscanning. RESULTS: The instructor-learner dyads showed greater interpersonal neural synchrony (INS) and learning acquisition in the intragenerational learning model in the difficult task condition (t (54) = 3.49, p < 0.01), whereas the two learning models yielded similar results in the easy condition (t (54) = 1.96, p = 0.06). In addition, INS and self-efficacy mediated the association between learning models and learning acquisition in older adults (b = 0.14, SEM = 0.04, 95 % CI [0.01 0.16]). DISCUSSION: This study is the first to provide evidence of interbrain synchrony in an investigation of the intragenerational learning model in older adults. Our findings suggest that intra-learning is as effective as traditional inter-learning and may be more effective in certain contexts, such as difficult tasks. Encouraging intra-learning in community service or educational activities can effectively mitigate the challenge of limited volunteers and enhance learning acquisition among older adults.

Neural basis of language familiarity effects on voice recognition: An fNIRS study.

Recognizing talkers' identity via speech is an important social skill in interpersonal interaction. Behavioral evidence has shown that listeners can identify better the voices of their native language than those of a non-native language, which is known as the language familiarity effect (LFE). However, its underlying neural mechanisms remain unclear. This study therefore investigated how the LFE occurs at the neural level by employing functional near-infrared spectroscopy (fNIRS). Late unbalanced bilinguals were first asked to learn to associate strangers' voices with their identities and then tested for recognizing the talkers' identities based on their voices speaking a language either highly familiar (i.e., native language Chinese), or moderately familiar (i.e., second language English), or completely unfamiliar (i.e., Ewe) to participants. Participants identified talkers the most accurately in Chinese and the least accurately in Ewe. Talker identification was quicker in Chinese than in English and Ewe but reaction time did not differ between the two non-native languages. At the neural level, recognizing voices speaking Chinese relative to English/Ewe produced less activity in the inferior frontal gyrus, precentral/postcentral gyrus, supramarginal gyrus, and superior temporal sulcus/gyrus while no difference was found between English and Ewe, indicating facilitation of voice identification by the automatic phonological encoding in the native language. These findings shed new light on the interrelations between language ability and voice recognition, revealing that the brain activation pattern of the LFE depends on the automaticity of language processing.

Right prefrontal activation associated with deviations from expected lipstick texture assessed with functional near-infrared spectroscopy.

Introduction: There is a continuous consumer demand for ever superior cosmetic products. In marketing, various forms of sensory evaluation are used to measure the consumer experience and provide data with which to improve cosmetics. Nonetheless, potential downsides of existing approaches have led to the exploration of the use of neuroimaging methods, such as functional near-infrared spectroscopy (fNIRS), to provide addition information about consumers' experiences with cosmetics. The aim of the present study was to investigate the feasibility of a real-time brain-based product evaluation method which detects the incongruency between a product, in this case lipstick, and a consumer's expectations. Method: Thirty healthy, female, habitual lipstick users were asked to apply six different lipsticks varying in softness and to rate the softness of and their willingness to pay (WTP) for each lipstick. Cerebral hemodynamic responses in frontal areas were measured with fNIRS during lipstick application and analyzed using the general linear model (GLM). Incongruency scores between softness and expectation were calculated in order to understand how far removed each lipstick was from a participant's optimal softness preference. The correlation between brain activation (beta scores) during the application of each lipstick and the respective incongruency scores from each participant were acquired using semi-partial correlation analysis, controlling for the effects of WTP. Results: We revealed a significant intra-subject correlation between incongruency scores and activation in the right inferior frontal gyrus (IFG). This confirms that as the texture incongruency scores increased for the lipstick samples, activation in each individual's right IFG also increased. Conclusion: The correlation observed between incongruency perceived by participants and activation of the right IFG not only suggests that the right IFG may play an important role in detecting incongruity when there is a discrepancy between the perceived texture and the consumer's expectations but also that measuring activity in the IFG may provide a new objective measurement of the consumer experience, thus contributing to the development of superior cosmetics.

An Ultralow-Power Real-Time Machine Learning Based fNIRS Motion Artifacts Detection.

Due to iterative matrix multiplications or gradient computations, machine learning modules often require a large amount of processing power and memory. As a result, they are often not feasible for use in wearable devices, which have limited processing power and memory. In this study, we propose an ultralow-power and real-time machine learning-based motion artifact detection module for functional near-infrared spectroscopy (fNIRS) systems. We achieved a high classification accuracy of 97.42%, low field-programmable gate array (FPGA) resource utilization of 38354 lookup tables and 6024 flip-flops, as well as low power consumption of 0.021 W in dynamic power. These results outperform conventional CPU support vector machine (SVM) methods and other state-of-the-art SVM implementations. This study has demonstrated that an FPGA-based fNIRS motion artifact classifier can be exploited while meeting low power and resource constraints, which are crucial in embedded hardware systems while keeping high classification accuracy.

Novel Feature Generation for Classification of Motor Activity from Functional Near-Infrared Spectroscopy Signals Using Machine Learning.

Recent research in the field of cognitive motor action decoding focuses on data acquired from Functional Near-Infrared Spectroscopy (fNIRS) and its analysis. This research aims to classify two different motor activities, namely, mental drawing (MD) and spatial navigation (SN), using fNIRS data from non-motor baseline data and other motor activities. Accurate activity detection in non-stationary signals like fNIRS is challenging and requires complex feature descriptors. As a novel framework, a new feature generation by fusion of wavelet feature, Hilbert, symlet, and Hjorth parameters is proposed for improving the accuracy of the classification. This new fused feature has statistical descriptor elements, time-localization in the frequency domain, edge feature, texture features, and phase information to detect and locate the activity accurately. Three types of independent component analysis, including FastICA, Picard, and Infomax were implemented for preprocessing which removes noises and motion artifacts. Two independent binary classifiers are designed to handle the complexity of classification in which one is responsible for mental drawing (MD) detection and the other one is spatial navigation (SN). Four different types of algorithms including nearest neighbors (KNN), Linear Discriminant Analysis (LDA), light gradient-boosting machine (LGBM), and Extreme Gradient Boosting (XGBOOST) were implemented. It has been identified that the LGBM classifier gives high accuracies-98% for mental drawing and 97% for spatial navigation. Comparison with existing research proves that the proposed method gives the highest classification accuracies. Statistical validation of the proposed new feature generation by the Kruskal-Wallis H-test and Mann-Whitney U non-parametric test proves the reliability of the proposed mechanism.

Prefrontal Cortex Responses to Social Video Stimuli in Young Children with and without Autism Spectrum Disorder.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder affecting individuals worldwide and characterized by deficits in social interaction along with the presence of restricted interest and repetitive behaviors. Despite decades of behavioral research, little is known about the brain mechanisms that influence social behaviors among children with ASD. This, in part, is due to limitations of traditional imaging techniques specifically targeting pediatric populations. As a portable and scalable optical brain monitoring technology, functional near infrared spectroscopy (fNIRS) provides a measure of cerebral hemodynamics related to sensory, motor, or cognitive function. Here, we utilized fNIRS to investigate the prefrontal cortex (PFC) activity of young children with ASD and with typical development while they watched social and nonsocial video clips. The PFC activity of ASD children was significantly higher for social stimuli at medial PFC, which is implicated in social cognition/processing. Moreover, this activity was also consistently correlated with clinical measures, and higher activation of the same brain area only during social video viewing was associated with more ASD symptoms. This is the first study to implement a neuroergonomics approach to investigate cognitive load in response to realistic, complex, and dynamic audiovisual social stimuli for young children with and without autism. Our results further confirm that new generation of portable fNIRS neuroimaging can be used for ecologically valid measurements of the brain function of toddlers and preschool children with ASD.

Enhancing Classification Accuracy with Integrated Contextual Gate Network: Deep Learning Approach for Functional Near-Infrared Spectroscopy Brain-Computer Interface Application.

Brain-computer interface (BCI) systems include signal acquisition, preprocessing, feature extraction, classification, and an application phase. In fNIRS-BCI systems, deep learning (DL) algorithms play a crucial role in enhancing accuracy. Unlike traditional machine learning (ML) classifiers, DL algorithms eliminate the need for manual feature extraction. DL neural networks automatically extract hidden patterns/features within a dataset to classify the data. In this study, a hand-gripping (closing and opening) two-class motor activity dataset from twenty healthy participants is acquired, and an integrated contextual gate network (ICGN) algorithm (proposed) is applied to that dataset to enhance the classification accuracy. The proposed algorithm extracts the features from the filtered data and generates the patterns based on the information from the previous cells within the network. Accordingly, classification is performed based on the similar generated patterns within the dataset. The accuracy of the proposed algorithm is compared with the long short-term memory (LSTM) and bidirectional long short-term memory (Bi-LSTM). The proposed ICGN algorithm yielded a classification accuracy of 91.23 ± 1.60%, which is significantly (p < 0.025) higher than the 84.89 ± 3.91 and 88.82 ± 1.96 achieved by LSTM and Bi-LSTM, respectively. An open access, three-class (right- and left-hand finger tapping and dominant foot tapping) dataset of 30 subjects is used to validate the proposed algorithm. The results show that ICGN can be efficiently used for the classification of two- and three-class problems in fNIRS-based BCI applications.

Hammerstein-Wiener Motion Artifact Correction for Functional Near-Infrared Spectroscopy: A Novel Inertial Measurement Unit-Based Technique.

Participant movement is a major source of artifacts in functional near-infrared spectroscopy (fNIRS) experiments. Mitigating the impact of motion artifacts (MAs) is crucial to estimate brain activity robustly. Here, we suggest and evaluate a novel application of the nonlinear Hammerstein-Wiener model to estimate and mitigate MAs in fNIRS signals from direct-movement recordings through IMU sensors mounted on the participant's head (head-IMU) and the fNIRS probe (probe-IMU). To this end, we analyzed the hemodynamic responses of single-channel oxyhemoglobin (HbO) and deoxyhemoglobin (HbR) signals from 17 participants who performed a hand tapping task with different levels of concurrent head movement. Additionally, the tapping task was performed without head movements to estimate the ground-truth brain activation. We compared the performance of our novel approach with the probe-IMU and head-IMU to eight established methods (PCA, tPCA, spline, spline Savitzky-Golay, wavelet, CBSI, RLOESS, and WCBSI) on four quality metrics: SNR, △AUC, RMSE, and R. Our proposed nonlinear Hammerstein-Wiener method achieved the best SNR increase (p < 0.001) among all methods. Visual inspection revealed that our approach mitigated MA contaminations that other techniques could not remove effectively. MA correction quality was comparable with head- and probe-IMUs.

Exercise promotes brain health: a systematic review of fNIRS studies.

Exercise can induce brain plasticity. Functional near-infrared spectroscopy (fNIRS) is a functional neuroimaging technique that exploits cerebral hemodynamics and has been widely used in the field of sports psychology to reveal the neural mechanisms underlying the effects of exercise. However, most existing fNIRS studies are cross-sectional and do not include exercise interventions. In addition, attributed to differences in experimental designs, the causal relationship between exercise and brain functions remains elusive. Hence, this systematic review aimed to determine the effects of exercise interventions on alterations in brain functional activity in healthy individuals using fNIRS and to determine the applicability of fNIRS in the research design of the effects of various exercise interventions on brain function. Scopus, Web of Science, PubMed, CNKI, Wanfang, and Weipu databases were searched for studies published up to June 15, 2021. This study was performed in accordance with the PRISMA guidelines. Two investigators independently selected articles and extracted relevant information. Disagreements were resolved by discussion with another author. Quality was assessed using the Cochrane risk-of-bias method. Data were pooled using random-effects models. A total of 29 studies were included in the analysis. Our results indicated that exercise interventions alter oxygenated hemoglobin levels in the prefrontal cortex and motor cortex, which are associated with improvements in higher cognitive functions (e.g., inhibitory control and working memory). The frontal cortex and motor cortex may be key regions for exercise-induced promotion of brain health. Future research is warranted on fluctuations in cerebral blood flow during exercise to elucidate the neural mechanism underlying the effects of exercise. Moreover, given that fNIRS is insensitive to motion, this technique is ideally suited for research during exercise interventions. Important factors include the study design, fNIRS device parameters, and exercise protocol. The examination of cerebral blood flow during exercise intervention is a future research direction that has the potential to identify cortical hemodynamic changes and elucidate the relationship between exercise and cognition. Future studies can combine multiple study designs to measure blood flow prior to and after exercise and during exercise in a more in-depth and comprehensive manner.