Neuron Selection Based on Deflection Coefficient Maximization for the Neural Decoding of Dexterous Finger Movements

Yong-Hee Kim,Thakor, Nitish V.,Schieber, Marc H.,Hyoung-Nam Kim IEEE 2015 IEEE transactions on neural systems and rehabilita Vol.23 No.3

<P>Future generations of brain-machine interface (BMI) will require more dexterous motion control such as hand and finger movements. Since a population of neurons in the primary motor cortex (M1) area is correlated with finger movements, neural activities recorded in M1 area are used to reconstruct an intended finger movement. In a BMI system, decoding discrete finger movements from a large number of input neurons does not guarantee a higher decoding accuracy in spite of the increase in computational burden. Hence, we hypothesize that selecting neurons important for coding dexterous flexion/extension of finger movements would improve the BMI performance. In this paper, two metrics are presented to quantitatively measure the importance of each neuron based on Bayes risk minimization and deflection coefficient maximization in a statistical decision problem. Since motor cortical neurons are active with movements of several different fingers, the proposed method is more suitable for a discrete decoding of flexion-extension finger movements than the previous methods for decoding reaching movements. In particular, the proposed metrics yielded high decoding accuracies across all subjects and also in the case of including six combined two-finger movements. While our data acquisition and analysis was done off-line and post processing, our results point to the significance of highly coding neurons in improving BMI performance.</P>

Insulin enhances neurite extension and myelination of diabetic neuropathy neurons

( Vuong M. Pham ),( Nitish Thakor ) 대한통증학회 2022 The Korean Journal of Pain Vol.35 No.2

Background: The authors established an in vitro model of diabetic neuropathy based on the culture system of primary neurons and Schwann cells (SCs) to mimic similar symptoms observed in in vivo models of this complication, such as impaired neurite extension and impaired myelination. The model was then utilized to investigate the effects of insulin on enhancing neurite extension and myelination of diabetic neurons. Methods: SCs and primary neurons were cultured under conditions mimicking hyperglycemia prepared by adding glucose to the basal culture medium. In a single culture, the proliferation and maturation of SCs and the neurite extension of neurons were evaluated. In a co-culture, the percentage of myelination of diabetic neurons was investigated. Insulin at different concentrations was supplemented to culture media to examine its effects on neurite extension and myelination. Results: The cells showed similar symptoms observed in in vivo models of this complication. In a single culture, hyperglycemia attenuated the proliferation and maturation of SCs, induced apoptosis, and impaired neurite extension of both sensory and motor neurons. In a co-culture of SCs and neurons, the percentage of myelinated neurites in the hyperglycemia-treated group was significantly lower than that in the control group. This impaired neurite extension and myelination was reversed by the introduction of insulin to the hyperglycemic culture media. Conclusions: Insulin may be a potential candidate for improving diabetic neuropathy. Insulin can function as a neurotrophic factor to support both neurons and SCs. Further research is needed to discover the potential of insulin in improving diabetic neuropathy.

Single-Finger Neural Basis Information-Based Neural Decoder for Multi-Finger Movements

Choi, Hwayoung,You, Kyung-Jin,Thakor, Nitish V.,Schieber, Marc H.,Shin, Hyun-Chool IEEE 2018 IEEE transactions on neural systems and rehabilita Vol.26 No.12

<P>In this paper, we investigate the relationship between single and multi-finger movements. By exploiting the neural correlation between the temporal firing patterns between movements, we show that the Pearson’s correlation coefficient for the physically related movement pairs are greater than those of others; the firing rates of the neurons that are tuned to a single-finger movements also increases when the corresponding multi-finger movements are instructed. We also use a hierarchical cluster analysis to verify not only the relationship between the single and multi-finger movements, but also the relationship between the flexion and extension movements. Furthermore, we propose a novel decoding method of modeling neural firing patterns while omitting the training process of the multi-finger movements. For the decoding, the Skellam and Gaussian probability distributions are used as mathematical models. The probabilistic distribution model of the multi-finger movements was estimated using the neural activity that was acquired during single-finger movements. As a result, the proposed neural decoding accuracy comparable with that of the supervised neural decoding accuracy when all of the neurons were used for the multi-finger movements. These results suggest that only the neural activities of single-finger movements can be exploited for the control of dexterous multi-finger neuroprosthetics.</P>

Motor Imagery based Brain-Computer Interface for Cerebellar Ataxia

Choi, Young-Seok,Shin, Hyun-Chool,Ying, Sarah H.,Newman, Geoffrey I.,Thakor, Nitish Korean Institute of Intelligent Systems 2014 한국지능시스템학회논문지 Vol.24 No.6

Cerebellar ataxia is a steadily progressive neurodegenerative disease associated with loss of motor control, leaving patients unable to walk, talk, or perform activities of daily living. Direct motor instruction in cerebella ataxia patients has limited effectiveness, presumably because an inappropriate closed-loop cerebellar response to the inevitable observed error confounds motor learning mechanisms. Recent studies have validated the age-old technique of employing motor imagery training (mental rehearsal of a movement) to boost motor performance in athletes, much as a champion downhill skier visualizes the course prior to embarking on a run. Could the use of EEG based BCI provide advanced biofeedback to improve motor imagery and provide a "backdoor" to improving motor performance in ataxia patients? In order to determine the feasibility of using EEG-based BCI control in this population, we compare the ability to modulate mu-band power (8-12 Hz) by performing a cued motor imagery task in an ataxia patient and healthy control. 소뇌 운동실조는 점차 진행되는 신경퇴행질병이며 운동 조절을 위한 기능의 상실을 동반하기에 환자의 삶을 심각하게 저하시킨다. 소뇌 운동실조 환자는 운동제어 과정에서 부적절한 폐회로 소뇌 반응으로 인해 운동 명령이 제한된다. 본 논문에서는 최근 뇌-컴퓨터 인터페이스 기술을 이용하여 소뇌의 이상으로 인한 운동실조 환자들이 외부기기를 제어할 수 있도록 운동상상 기반의 뇌파의 특성을 분석하고 이를 이용한 뇌-컴퓨터 인터페이스 기법을 제안한다. 뇌파 기반의 뇌-컴퓨터 인터페이스의 효용성을 검증하기 위하여 소뇌 운동실조 환자와 정상인 그룹에서 운동상상에 따른 뮤밴드 파워를 조절하는 능력을 비교하였다. 이를 통하여 소뇌 운동실조 환자에의 뇌-컴퓨터 인터페이스의 가능성을 보여준다.

Neural Decoding of Finger Movements Using Skellam-Based Maximum-Likelihood Decoding

Shin, Hyun-Chool,Aggarwal, Vikram,Acharya, Soumyadipta,Schieber, Marc H.,Thakor $^$, Nitish V. IEEE 2010 IEEE Transactions on Biomedical Engineering Vol.57 No.3

<P>We present an optimal method for decoding the activity of primary motor cortex (M1) neurons in a nonhuman primate during single finger movements. The method is based on the maximum-likelihood (ML) inference, which assuming the probability of finger movements is uniform, is equivalent to the maximum <I>a posteriori</I> (MAP) inference. Each neuron's activation is first quantified by the change in firing rate before and after finger movement. We then estimate the probability density function of this activation given finger movement, i.e., Pr(neuronal activation (x)| finger movements (m)). Based on the ML criterion, we choose finger movements to maximize Pr(x|m). Experimentally, data were collected from 115 task-related neurons in M1 as the monkey performed flexion and extension of each finger and the wrist (12 movements). With as few as 20-25 randomly selected neurons, the proposed method decoded single-finger movements with 99% accuracy. Since the training and decoding procedures in the proposed method are simple and computationally efficient, the method can be extended for real-time neuroprosthetic control of a dexterous hand.</P>

Motor Imagery based Brain-Computer Interface for Cerebellar Ataxia

Young-Seok Choi(최영석),Hyun-Chool Shin(신현출),Sarah H. Ying,Geoffrey I. Newman,Nitish Thakor 한국지능시스템학회 2014 한국지능시스템학회논문지 Vol.24 No.6

소뇌 운동실조는 점차 진행되는 신경퇴행질병이며 운동 조절을 위한 기능의 상실을 동반하기에 환자의 삶을 심각하게 저하시킨다. 소뇌 운동실조 환자는 운동제어 과정에서 부적절한 폐회로 소뇌 반응으로 인해 운동 명령이 제한된다. 본 논문에서는 최근 뇌-컴퓨터 인터페이스 기술을 이용하여 소뇌의 이상으로 인한 운동실조 환자들이 외부기기를 제어할 수 있도록 운동상상 기반의 뇌파의 특성을 분석하고 이를 이용한 뇌-컴퓨터 인터페이스 기법을 제안한다. 뇌파 기반의 뇌-컴퓨터 인터페이스의 효용성을 검증하기 위하여 소뇌 운동실조 환자와 정상인 그룹에서 운동상상에 따른 뮤밴드 파워를 조절하는 능력을 비교하였다. 이를 통하여 소뇌 운동실조 환자에의 뇌-컴퓨터 인터페이스의 가능성을 보여준다. Cerebellar ataxia is a steadily progressive neurodegenerative disease associated with loss of motor control, leaving patients unable to walk, talk, or perform activities of daily living. Direct motor instruction in cerebella ataxia patients has limited effectiveness, presumably because an inappropriate closed-loop cerebellar response to the inevitable observed error confounds motor learning mechanisms. Recent studies have validated the age-old technique of employing motor imagery training (mental rehearsal of a movement) to boost motor performance in athletes, much as a champion downhill skier visualizes the course prior to embarking on a run. Could the use of EEG based BCI provide advanced biofeedback to improve motor imagery and provide a “backdoor” to improving motor performance in ataxia patients? In order to determine the feasibility of using EEG-based BCI control in this population, we compare the ability to modulate mu-band power (8-12 Hz) by performing a cued motor imagery task in an ataxia patient and healthy control.

Motor Imagery based Brain-Computer Interface for Cerebellar Ataxia

Young-Seok Choi(최영석),Hyun-Chool Shin(신현출),Sarah H. Ying,Geoffrey I. Newman,Nitish Thakor 한국지능시스템학회 2014 한국지능시스템학회 학술발표 논문집 Vol.24 No.2