Comparison of Optical and Concentration Feature Used for fNIRS-Based BCI System Using HMM

Xu, Baolei; Fu, Yun; Shi, Gang; Yin, Xu; Miao, Lingjuan; 王, 志東; Li, Hong Yi

doi:10.4028/www.scientific.net/amm.385-386.1443

Cited by 3 publications

(4 citation statements)

References 24 publications

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“…Many fNIRS BCI studies to date (e.g., Holper and Wolf, 2011 ; Fazli et al, 2012 ; Xu et al, 2014 ) employ subject-dependent BCIs, i.e., trained on data from the subject in question, either from parts of the test session itself or from separate sessions of the same subject. The latter scenario avoids some issues in how preprocessing (e.g., whole-data statistics), trial selection (e.g., randomized cross-validation), or reduced cap fit variability within-session might lead to overestimating BCI performance relative to a full session-to-session transfer setting.…”

Section: Discussionmentioning

confidence: 99%

“…Similarly in motor execution paradigms, the majority of studies also utilize 10 to 20 s (or longer) trials, with few exceptions including Huppert et al (2006) and Gagnon et al (2012Gagnon et al ( , 2014 (2, 2, and 5 s, respectively). Finger tapping, as studied here, is one of the most common types of hand motor studies in the literature (e.g., Sitaram et al, 2007;Holper and Wolf, 2011;Wu et al, 2018;Bak et al, 2019;Kwon et al, 2020), although we note that alternatives such as fist clenching, e.g., studied by Xu et al (2014) or squeezing a ball or soft prop (e.g., Coyle et al, 2007;Batula et al, 2017) have also been used.…”

Section: Short-duration Hand Motor Imagery Tasksmentioning

confidence: 99%

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On decoding of rapid motor imagery in a diverse population using a high-density NIRS device

Kothe,

Hanada,

Mullen

et al. 2024

Front. Neuroergonomics

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IntroductionFunctional near-infrared spectroscopy (fNIRS) aims to infer cognitive states such as the type of movement imagined by a study participant in a given trial using an optical method that can differentiate between oxygenation states of blood in the brain and thereby indirectly between neuronal activity levels. We present findings from an fNIRS study that aimed to test the applicability of a high-density (>3000 channels) NIRS device for use in short-duration (2 s) left/right hand motor imagery decoding in a diverse, but not explicitly balanced, subject population. A side aim was to assess relationships between data quality, self-reported demographic characteristics, and brain-computer interface (BCI) performance, with no subjects rejected from recruitment or analysis.MethodsBCI performance was quantified using several published methods, including subject-specific and subject-independent approaches, along with a high-density fNIRS decoder previously validated in a separate study.ResultsWe found that decoding of motor imagery on this population proved extremely challenging across all tested methods. Overall accuracy of the best-performing method (the high-density decoder) was 59.1 +/– 6.7% after excluding subjects where almost no optode-scalp contact was made over motor cortex and 54.7 +/– 7.6% when all recorded sessions were included. Deeper investigation revealed that signal quality, hemodynamic responses, and BCI performance were all strongly impacted by the hair phenotypical and demographic factors under investigation, with over half of variance in signal quality explained by demographic factors alone.DiscussionOur results contribute to the literature reporting on challenges in using current-generation NIRS devices on subjects with long, dense, dark, and less pliable hair types along with the resulting potential for bias. Our findings confirm the need for increased focus on these populations, accurate reporting of data rejection choices across subject intake, curation, and final analysis in general, and signal a need for NIRS optode designs better optimized for the general population to facilitate more robust and inclusive research outcomes.

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Section: Discussionmentioning

confidence: 99%

Section: Short-duration Hand Motor Imagery Tasksmentioning

confidence: 99%

On decoding of rapid motor imagery in a diverse population using a high-density NIRS device

Kothe,

Hanada,

Mullen

et al. 2024

Front. Neuroergonomics

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“…The received symbol can be observed, but the state in which an error happens is not observable. HMMs were used for modeling the statistics of burst errors in the communication channel [24][25][26][27][28][29][30] . The advantage of HMM compared to the standard wireless channel simulation is its high advance in simulation time.…”

Section: Figurementioning

confidence: 99%

Evaluating the CDMA System Using Hidden Markov and Semi Hidden Markov Models

Kordnoori

Mostafaei

Kordnoori

et al. 2021

JTS

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CDMA is an important and basic part of today's communications technologies. This technology can be analyzed efficiently by reducing the time, computation burden, and cost by characterizing the physical layer with a Markov Model. Waveform level simulation is generally used for simulating different parts of a digital communication system. In this paper, we introduce two different mathematical methods to model digital communication channels. Hidden Markov and Semi Hidden Markov models' applications have been investigated for evaluating the DS-CDMA link performance with different parameters. Hidden Markov Models have been a powerful mathematical tool that can be applied as models of discrete-time series in many fields successfully. A semi-hidden Markov model as a stochastic process is a modification of hidden Markov models with states that are no longer unobservable and less hidden. A principal characteristic of this mathematical model is statistical inertia, which admits the generation, and analysis of observation symbol contains frequent runs.

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“…Motor imagery (MI) is defined as the cognitive activity in which a subject imagines a movement without actually performing the movement (Vries and Mulder, 2007), and it is a common application paradigm in the field of braincomputer interface research (Mattia et al, 2012). This method realizes communication and control with external devices by imaging body movements (Xu et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Research on Rehabilitation Training Strategies Using Multimodal Virtual Scene Stimulation

Xie

Wang

et al. 2022

Front. Aging Neurosci.

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It is difficult for stroke patients with flaccid paralysis to receive passive rehabilitation training. Therefore, virtual rehabilitation technology that integrates the motor imagery brain-computer interface and virtual reality technology has been applied to the field of stroke rehabilitation and has evolved into a physical rehabilitation training method. This virtual rehabilitation technology can enhance the initiative and adaptability of patient rehabilitation. To maximize the deep activation of the subjects motor nerves and accelerate the remodeling mechanism of motor nerve function, this study designed a brain-computer interface rehabilitation training strategy using different virtual scenes, including static scenes, dynamic scenes, and VR scenes. Including static scenes, dynamic scenes, and VR scenes. We compared and analyzed the degree of neural activation and the recognition rate of motor imagery in stroke patients after motor imagery training using stimulation of different virtual scenes, The results show that under the three scenarios, The order of degree of neural activation and the recognition rate of motor imagery from high to low is: VR scenes, dynamic scenes, static scenes. This paper provided the research basis for a virtual rehabilitation strategy that could integrate the motor imagery brain-computer interface and virtual reality technology.

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Comparison of Optical and Concentration Feature Used for fNIRS-Based BCI System Using HMM

Cited by 3 publications

References 24 publications

On decoding of rapid motor imagery in a diverse population using a high-density NIRS device

On decoding of rapid motor imagery in a diverse population using a high-density NIRS device

Evaluating the CDMA System Using Hidden Markov and Semi Hidden Markov Models

Research on Rehabilitation Training Strategies Using Multimodal Virtual Scene Stimulation

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