Literature DB >> 24503623

Subject-specific modulation of local field potential spectral power during brain-machine interface control in primates.

Kelvin So1, Siddharth Dangi, Amy L Orsborn, Michael C Gastpar, Jose M Carmena.   

Abstract

OBJECTIVE: Intracortical brain-machine interfaces (BMIs) have predominantly utilized spike activity as the control signal. However, an increasing number of studies have shown the utility of local field potentials (LFPs) for decoding motor related signals. Currently, it is unclear how well different LFP frequencies can serve as features for continuous, closed-loop BMI control. APPROACH: We demonstrate 2D continuous LFP-based BMI control using closed-loop decoder adaptation, which adapts decoder parameters to subject-specific LFP feature modulations during BMI control. We trained two macaque monkeys to control a 2D cursor in a center-out task by modulating LFP power in the 0-150 Hz range. MAIN
RESULTS: While both monkeys attained control, they used different strategies involving different frequency bands. One monkey primarily utilized the low-frequency spectrum (0-80 Hz), which was highly correlated between channels, and obtained proficient performance even with a single channel. In contrast, the other monkey relied more on higher frequencies (80-150 Hz), which were less correlated between channels, and had greater difficulty with control as the number of channels decreased. We then restricted the monkeys to use only various sub-ranges (0-40, 40-80, and 80-150 Hz) of the 0-150 Hz band. Interestingly, although both monkeys performed better with some sub-ranges than others, they were able to achieve BMI control with all sub-ranges after decoder adaptation, demonstrating broad flexibility in the frequencies that could potentially be used for LFP-based BMI control. SIGNIFICANCE: Overall, our results demonstrate proficient, continuous BMI control using LFPs and provide insight into the subject-specific spectral patterns of LFP activity modulated during control.

Mesh:

Year:  2014        PMID: 24503623     DOI: 10.1088/1741-2560/11/2/026002

Source DB:  PubMed          Journal:  J Neural Eng        ISSN: 1741-2552            Impact factor:   5.379


  28 in total

1.  A low-power band of neuronal spiking activity dominated by local single units improves the performance of brain-machine interfaces.

Authors:  Samuel R Nason; Alex K Vaskov; Matthew S Willsey; Elissa J Welle; Hyochan An; Philip P Vu; Autumn J Bullard; Chrono S Nu; Jonathan C Kao; Krishna V Shenoy; Taekwang Jang; Hun-Seok Kim; David Blaauw; Parag G Patil; Cynthia A Chestek
Journal:  Nat Biomed Eng       Date:  2020-07-27       Impact factor: 25.671

2.  A high performing brain-machine interface driven by low-frequency local field potentials alone and together with spikes.

Authors:  Sergey D Stavisky; Jonathan C Kao; Paul Nuyujukian; Stephen I Ryu; Krishna V Shenoy
Journal:  J Neural Eng       Date:  2015-05-06       Impact factor: 5.379

Review 3.  Physiological properties of brain-machine interface input signals.

Authors:  Marc W Slutzky; Robert D Flint
Journal:  J Neurophysiol       Date:  2017-06-14       Impact factor: 2.714

4.  Frequency Shifts and Depth Dependence of Premotor Beta Band Activity during Perceptual Decision-Making.

Authors:  Chandramouli Chandrasekaran; Iliana E Bray; Krishna V Shenoy
Journal:  J Neurosci       Date:  2019-01-03       Impact factor: 6.167

5.  Mood variations decoded from multi-site intracranial human brain activity.

Authors:  Omid G Sani; Yuxiao Yang; Morgan B Lee; Heather E Dawes; Edward F Chang; Maryam M Shanechi
Journal:  Nat Biotechnol       Date:  2018-09-10       Impact factor: 54.908

Review 6.  Brain-computer interfaces for communication and rehabilitation.

Authors:  Ujwal Chaudhary; Niels Birbaumer; Ander Ramos-Murguialday
Journal:  Nat Rev Neurol       Date:  2016-08-19       Impact factor: 42.937

7.  Decoding of intended saccade direction in an oculomotor brain-computer interface.

Authors:  Nan Jia; Scott L Brincat; Andrés F Salazar-Gómez; Mikhail Panko; Frank H Guenther; Earl K Miller
Journal:  J Neural Eng       Date:  2017-08       Impact factor: 5.379

Review 8.  Neuroplasticity subserving the operation of brain-machine interfaces.

Authors:  Karim G Oweiss; Islam S Badreldin
Journal:  Neurobiol Dis       Date:  2015-05-09       Impact factor: 5.996

9.  Continuous decoding of human grasp kinematics using epidural and subdural signals.

Authors:  Robert D Flint; Joshua M Rosenow; Matthew C Tate; Marc W Slutzky
Journal:  J Neural Eng       Date:  2016-11-30       Impact factor: 5.379

10.  Beta band oscillations in motor cortex reflect neural population signals that delay movement onset.

Authors:  Preeya Khanna; Jose M Carmena
Journal:  Elife       Date:  2017-05-03       Impact factor: 8.140

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