Decoding Multiple Sound-Categories in the Auditory Cortex by Neural Networks: An fNIRS Study

Yoo, So-Hyeon; Santosa, Hendrik; Kim, Chang-Seok

doi:10.3389/fnhum.2021.636191

Cited by 16 publications

(7 citation statements)

References 78 publications

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“…Furthermore, a t -test was applied to student participants. This test checks the statistical significance of the attained results [ 54 ]. The confidence interval is specified at 95% ( p < 0.05).…”

Section: Resultsmentioning

confidence: 99%

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fNIRS-Based Upper Limb Motion Intention Recognition Using an Artificial Neural Network for Transhumeral Amputees

Sattar

Kausar

Usama

et al. 2022

Sensors

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Prosthetic arms are designed to assist amputated individuals in the performance of the activities of daily life. Brain machine interfaces are currently employed to enhance the accuracy as well as number of control commands for upper limb prostheses. However, the motion prediction for prosthetic arms and the rehabilitation of amputees suffering from transhumeral amputations is limited. In this paper, functional near-infrared spectroscopy (fNIRS)-based approach for the recognition of human intention for six upper limb motions is proposed. The data were extracted from the study of fifteen healthy subjects and three transhumeral amputees for elbow extension, elbow flexion, wrist pronation, wrist supination, hand open, and hand close. The fNIRS signals were acquired from the motor cortex region of the brain by the commercial NIRSport device. The acquired data samples were filtered using finite impulse response (FIR) filter. Furthermore, signal mean, signal peak and minimum values were computed as feature set. An artificial neural network (ANN) was applied to these data samples. The results show the likelihood of classifying the six arm actions with an accuracy of 78%. The attained results have not yet been reported in any identical study. These achieved fNIRS results for intention detection are promising and suggest that they can be applied for the real-time control of the transhumeral prosthesis.

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Section: Resultsmentioning

confidence: 99%

“…The output from the second layer is 12 × 128. Subsequently, the global average pooling is applied between the output layer for which the Adam optimization method was deployed [ 53 , 54 ].…”

Section: Feature Extraction and Classification Of Motion Intention Si...mentioning

confidence: 99%

fNIRS-Based Upper Limb Motion Intention Recognition Using an Artificial Neural Network for Transhumeral Amputees

Sattar

Kausar

Usama

et al. 2022

Sensors

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show abstract

“…Further work extended use of fNIRS to capture the hemodynamic response to sound in auditory cortex in children and infants. 95 96 97 Differential activations to various sound categories in auditory cortex have also been confirmed using fNIRS, 98 99 indicating that while fNIRS has lower spatial resolution than fMRI, it can be quite sensitive to changes in the type of sound stimulus being used. To our knowledge, no study to date has confirmed whether fNIRS has the necessary sensitivity for tonotopic mapping of the auditory cortex.…”

Section: Introduction To Functional Near-infrared Spectroscopymentioning

confidence: 83%

Brightening the Study of Listening Effort with Functional Near-Infrared Spectroscopy: A Scoping Review

Russo

2023

Semin Hear

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Listening effort is a long-standing area of interest in auditory cognitive neuroscience. Prior research has used multiple techniques to shed light on the neurophysiological mechanisms underlying listening during challenging conditions. Functional near-infrared spectroscopy (fNIRS) is growing in popularity as a tool for cognitive neuroscience research, and its recent advances offer many potential advantages over other neuroimaging modalities for research related to listening effort. This review introduces the basic science of fNIRS and its uses for auditory cognitive neuroscience. We also discuss its application in recently published studies on listening effort and consider future opportunities for studying effortful listening with fNIRS. After reading this article, the learner will know how fNIRS works and summarize its uses for listening effort research. The learner will also be able to apply this knowledge toward generation of future research in this area.

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“…For classification tasks, the cross-entropy loss function 27 measures the difference in the probability distributions between ground truths and network predictions. Multiclass classifications 28 – 31 use categorical cross-entropy as the loss function, whereas binary classification problems use binary cross-entropy. For regression 32 or reconstruction 33 problems, the mean squared error loss function is used.…”

Section: Deep Learning Methodologymentioning

confidence: 99%

Deep learning in fNIRS: a review

Eastmond

Subedi

et al. 2022

Neurophoton.

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Significance: Optical neuroimaging has become a well-established clinical and research tool to monitor cortical activations in the human brain. It is notable that outcomes of functional nearinfrared spectroscopy (fNIRS) studies depend heavily on the data processing pipeline and classification model employed. Recently, deep learning (DL) methodologies have demonstrated fast and accurate performances in data processing and classification tasks across many biomedical fields.Aim: We aim to review the emerging DL applications in fNIRS studies.Approach: We first introduce some of the commonly used DL techniques. Then, the review summarizes current DL work in some of the most active areas of this field, including braincomputer interface, neuro-impairment diagnosis, and neuroscience discovery.Results: Of the 63 papers considered in this review, 32 report a comparative study of DL techniques to traditional machine learning techniques where 26 have been shown outperforming the latter in terms of the classification accuracy. In addition, eight studies also utilize DL to reduce the amount of preprocessing typically done with fNIRS data or increase the amount of data via data augmentation. Conclusions:The application of DL techniques to fNIRS studies has shown to mitigate many of the hurdles present in fNIRS studies such as lengthy data preprocessing or small sample sizes while achieving comparable or improved classification accuracy.

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Decoding Multiple Sound-Categories in the Auditory Cortex by Neural Networks: An fNIRS Study

Cited by 16 publications

References 78 publications

fNIRS-Based Upper Limb Motion Intention Recognition Using an Artificial Neural Network for Transhumeral Amputees

fNIRS-Based Upper Limb Motion Intention Recognition Using an Artificial Neural Network for Transhumeral Amputees

Brightening the Study of Listening Effort with Functional Near-Infrared Spectroscopy: A Scoping Review

Deep learning in fNIRS: a review

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