Classification of sensorimotor cortex signals based on the task durations: an fNIRS-BCI study

2021 ◽  
Author(s):  
M. N. Afzal Khan ◽  
Usman Ghafoor ◽  
Keum-Shik Hong
Keyword(s):  
Sensorimotor Cortex ◽  
Task Durations

Alterations in post-movement beta event related synchronization throughout the migraine cycle: A controlled, longitudinal study

Cephalalgia ◽  
2017 ◽  
Vol 38 (4) ◽  
pp. 718-729 ◽  
Author(s):  
Martin Syvertsen Mykland ◽  
Marte Helene Bjørk ◽  
Marit Stjern ◽  
Trond Sand
Keyword(s):  
Sensorimotor Cortex ◽  
Repeated Measures ◽  
Cortical Excitability ◽  
T Test ◽  
The Difference ◽  
Student’S T ◽  
The Right ◽  
Cyclic Changes ◽  
Student’S T Test

Background The migraine brain is believed to have altered cortical excitability compared to controls and between migraine cycle phases. Our aim was to evaluate post-activation excitability through post-movement beta event related synchronization (PMBS) in sensorimotor cortices with and without sensory discrimination. Subjects and methods We recorded EEG of 41 migraine patients and 31 healthy controls on three different days with classification of days in relation to migraine phases. During each recording, subjects performed one motor and one sensorimotor task with the right wrist. Controls and migraine patients in the interictal phase were compared with repeated measures (R-) ANOVA and two sample Student’s t-test. Migraine phases were compared to the interictal phase with R-ANOVA and paired Student’s t-test. Results The difference between PMBS at the contralateral and ipsilateral sensorimotor cortex was altered throughout the migraine cycle. Compared to the interictal phase, we found decreased PMBS at the ipsilateral sensorimotor cortex in the ictal phase and increased PMBS in the preictal phase. Lower ictal PMBS was found in bilateral sensorimotor cortices in patients with right side headache predominance. Conclusion The cyclic changes of PMBS in migraine patients may indicate that a dysfunction in deactivation and interhemispheric inhibition of the sensorimotor cortex is involved in the migraine attack cascade.

scholarly journals Classification and characterisation of brain network changes in chronic back pain: A multicenter study

2018 ◽  
Vol 3 ◽  
pp. 19
Author(s):  
Hiroaki Mano ◽  
Gopal Kotecha ◽  
Kenji Leibnitz ◽  
Takashi Matsubara ◽  
Aya Nakae ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chronic Pain ◽  
Deep Learning ◽  
Back Pain ◽  
Sensorimotor Cortex ◽  
Network Architecture ◽  
Chronic Back Pain ◽  
Open Data ◽  
Brain Network

Background. Chronic pain is a common, often disabling condition thought to involve a combination of peripheral and central neurobiological factors. However, the extent and nature of changes in the brain is poorly understood. Methods. We investigated brain network architecture using resting-state fMRI data in chronic back pain patients in the UK and Japan (41 patients, 56 controls), as well as open data from USA. We applied machine learning and deep learning (conditional variational autoencoder architecture) methods to explore classification of patients/controls based on network connectivity. We then studied the network topology of the data, and developed a multislice modularity method to look for consensus evidence of modular reorganisation in chronic back pain. Results. Machine learning and deep learning allowed reliable classification of patients in a third, independent open data set with an accuracy of 63%, with 68% in cross validation of all data. We identified robust evidence of network hub disruption in chronic pain, most consistently with respect to clustering coefficient and betweenness centrality. We found a consensus pattern of modular reorganisation involving extensive, bilateral regions of sensorimotor cortex, and characterised primarily by negative reorganisation - a tendency for sensorimotor cortex nodes to be less inclined to form pairwise modular links with other brain nodes. In contrast, intraparietal sulcus displayed a propensity towards positive modular reorganisation, suggesting that it might have a role in forming modules associated with the chronic pain state. Conclusion. The results provide evidence of consistent and characteristic brain network changes in chronic pain, characterised primarily by extensive reorganisation of the network architecture of the sensorimotor cortex.

scholarly journals Automated classification of pain perception using high-density electroencephalography data

2017 ◽  
Vol 117 (2) ◽  
pp. 786-795 ◽  
Author(s):  
Gaurav Misra ◽  
Wei-en Wang ◽  
Derek B. Archer ◽  
Arnab Roy ◽  
Stephen A. Coombes
Keyword(s):  
Prefrontal Cortex ◽  
Biological Markers ◽  
Sensorimotor Cortex ◽  
Pain Perception ◽  
High Density ◽  
Cortical Region ◽  
Automated Classification ◽  
Beta Power ◽  
Eeg Data

The translation of brief, millisecond-long pain-eliciting stimuli to the subjective perception of pain is associated with changes in theta, alpha, beta, and gamma oscillations over sensorimotor cortex. However, when a pain-eliciting stimulus continues for minutes, regions beyond the sensorimotor cortex, such as the prefrontal cortex, are also engaged. Abnormalities in prefrontal cortex have been associated with chronic pain states, but conventional, millisecond-long EEG paradigms do not engage prefrontal regions. In the current study, we collected high-density EEG data during an experimental paradigm in which subjects experienced a 4-s, low- or high-intensity pain-eliciting stimulus. EEG data were analyzed using independent component analyses, EEG source localization analyses, and measure projection analyses. We report three novel findings. First, an increase in pain perception was associated with an increase in gamma and theta power in a cortical region that included medial prefrontal cortex. Second, a decrease in lower beta power was associated with an increase in pain perception in a cortical region that included the contralateral sensorimotor cortex. Third, we used machine learning for automated classification of EEG data into low- and high-pain classes. Theta and gamma power in the medial prefrontal region and lower beta power in the contralateral sensorimotor region served as features for classification. We found a leave-one-out cross-validation accuracy of 89.58%. The development of biological markers for pain states continues to gain traction in the literature, and our findings provide new information that advances this body of work.NEW & NOTEWORTHY The development of a biological marker for pain continues to gain traction in literature. Our findings show that high- and low-pain perception in human subjects can be classified with 89% accuracy using high-density EEG data from prefrontal cortex and contralateral sensorimotor cortex. Our approach represents a novel neurophysiological paradigm that advances the literature on biological markers for pain.

scholarly journals Classification of Articulator Movements and Movement Direction from Sensorimotor Cortex Activity

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
E. Salari ◽  
Z. V. Freudenburg ◽  
M. P. Branco ◽  
E. J. Aarnoutse ◽  
M. J. Vansteensel ◽  
...  
Keyword(s):  
Neural Activity ◽  
Sensorimotor Cortex ◽  
Muscle Activity ◽  
Cortical Area ◽  
Brain Activity ◽  
Activity Patterns ◽  
Movement Direction ◽  
Computer Interfaces ◽  
Small Parts

Abstract For people suffering from severe paralysis, communication can be difficult or nearly impossible. Technology systems called brain-computer interfaces (BCIs) are being developed to assist these people with communication by using their brain activity to control a computer without any muscle activity. To benefit the development of BCIs that employ neural activity related to speech, we investigated if neural activity patterns related to different articulator movements can be distinguished from each other. We recorded with electrocorticography (ECoG), the neural activity related to different articulator movements in 4 epilepsy patients and classified which articulator participants moved based on the sensorimotor cortex activity patterns. The same was done for different movement directions of a single articulator, the tongue. In both experiments highly accurate classification was obtained, on average 92% for different articulators and 85% for different tongue directions. Furthermore, the data show that only a small part of the sensorimotor cortex is needed for classification (ca. 1 cm2). We show that recordings from small parts of the sensorimotor cortex contain information about different articulator movements which might be used for BCI control. Our results are of interest for BCI systems that aim to decode neural activity related to (actual or attempted) movements from a contained cortical area.

scholarly journals Classification and characterisation of brain network changes in chronic back pain: A multicenter study

2018 ◽  
Vol 3 ◽  
pp. 19 ◽  
Author(s):  
Hiroaki Mano ◽  
Gopal Kotecha ◽  
Kenji Leibnitz ◽  
Takashi Matsubara ◽  
Christian Sprenger ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chronic Pain ◽  
Deep Learning ◽  
Back Pain ◽  
Sensorimotor Cortex ◽  
Network Architecture ◽  
Chronic Back Pain ◽  
Open Data ◽  
Brain Network

Background. Chronic pain is a common, often disabling condition thought to involve a combination of peripheral and central neurobiological factors. However, the extent and nature of changes in the brain is poorly understood. Methods. We investigated brain network architecture using resting-state fMRI data in chronic back pain patients in the UK and Japan (41 patients, 56 controls), as well as open data from USA. We applied machine learning and deep learning (conditional variational autoencoder architecture) methods to explore classification of patients/controls based on network connectivity. We then studied the network topology of the data, and developed a multislice modularity method to look for consensus evidence of modular reorganisation in chronic back pain. Results. Machine learning and deep learning allowed reliable classification of patients in a third, independent open data set with an accuracy of 63%, with 68% in cross validation of all data. We identified robust evidence of network hub disruption in chronic pain, most consistently with respect to clustering coefficient and betweenness centrality. We found a consensus pattern of modular reorganisation involving extensive, bilateral regions of sensorimotor cortex, and characterised primarily by negative reorganisation - a tendency for sensorimotor cortex nodes to be less inclined to form pairwise modular links with other brain nodes. Furthermore, these regions were found to display increased connectivity with the pregenual anterior cingulate cortex, a region known to be involved in endogenous pain control. In contrast, intraparietal sulcus displayed a propensity towards positive modular reorganisation, suggesting that it might have a role in forming modules associated with the chronic pain state. Conclusion. The results provide evidence of consistent and characteristic brain network changes in chronic pain, characterised primarily by extensive reorganisation of the network architecture of the sensorimotor cortex.

Note on the spectral classification of carbon stars in the photographic infrared region

1966 ◽  
Vol 24 ◽  
pp. 21-23
Author(s):  
Y. Fujita
Keyword(s):  
Infrared Region ◽  
Spectral Classification ◽  
Carbon Stars

We have investigated the spectrograms (dispersion: 8Å/mm) in the photographic infrared region fromλ7500 toλ9000 of some carbon stars obtained by the coudé spectrograph of the 74-inch reflector attached to the Okayama Astrophysical Observatory. The names of the stars investigated are listed in Table 1.

Diagnostic Value of Electron Microscopy in Soft Tissue Tumors

1970 ◽  
Vol 28 ◽  
pp. 220-221
Author(s):  
Gerald Fine ◽  
Azorides R. Morales
Keyword(s):  
Cardiac Myxoma ◽  
Osteogenic Sarcoma ◽  
Diagnostic Value ◽  
Soft Tissue Tumors ◽  
Amelanotic Melanoma ◽  
Growth Patterns ◽  
Light Microscopic Level ◽  
Mesenchymal Tumors ◽  
Good Agreement

For years the separation of carcinoma and sarcoma and the subclassification of sarcomas has been based on the appearance of the tumor cells and their microscopic growth pattern and information derived from certain histochemical and special stains. Although this method of study has produced good agreement among pathologists in the separation of carcinoma from sarcoma, it has given less uniform results in the subclassification of sarcomas. There remain examples of neoplasms of different histogenesis, the classification of which is questionable because of similar cytologic and growth patterns at the light microscopic level; i.e. amelanotic melanoma versus carcinoma and occasionally sarcoma, sarcomas with an epithelial pattern of growth simulating carcinoma, histologically similar mesenchymal tumors of different histogenesis (histiocytoma versus rhabdomyosarcoma, lytic osteogenic sarcoma versus rhabdomyosarcoma), and myxomatous mesenchymal tumors of diverse histogenesis (myxoid rhabdo and liposarcomas, cardiac myxoma, myxoid neurofibroma, etc.)

Ultrastructure of mesotheliomas

1984 ◽  
Vol 42 ◽  
pp. 98-101
Author(s):  
Irving Dardick
Keyword(s):  
Electron Microscopy ◽  
Latent Period ◽  
Spindle Cell ◽  
Spindle Cell Sarcoma ◽  
Metastatic Adenocarcinoma ◽  
Cell Sarcoma ◽  
Cytological Features ◽  
Industrial Use ◽  
Degree Of Differentiation

With the extensive industrial use of asbestos in this century and the long latent period (20-50 years) between exposure and tumor presentation, the incidence of malignant mesothelioma is now increasing. Thus, surgical pathologists are more frequently faced with the dilemma of differentiating mesothelioma from metastatic adenocarcinoma and spindle-cell sarcoma involving serosal surfaces. Electron microscopy is amodality useful in clarifying this problem.In utilizing ultrastructural features in the diagnosis of mesothelioma, it is essential to appreciate that the classification of this tumor reflects a variety of morphologic forms of differing biologic behavior (Table 1). Furthermore, with the variable histology and degree of differentiation in mesotheliomas it might be expected that the ultrastructure of such tumors also reflects a range of cytological features. Such is the case.

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