Neural Activity from Attention Networks Predicts Movement Errors

Breault, Macauley Smith; González-Martínez, Jorge; Gale, John T.; Sarma, Sridevi V.

doi:10.1109/embc.2019.8856958

Cited by 2 publications

(2 citation statements)

References 25 publications

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“…Subjects performed goal-directed reaching movements with speed instructions that have been previously described (Johnson et al, 2014; Breault et al, 2017, 2018, 2019a,b; Kerr et al, 2017). Movements were made using a robotic manipulandum from the InMotion ARM Interactive Therapy System (Interactive Motion Technologies, Watertown, MA, USA) and were displayed as a cursor on an attached computer screen (Figure 2A).…”

Section: Methodsmentioning

confidence: 99%

Non-motor Brain Regions in Non-dominant Hemisphere Are Influential in Decoding Movement Speed

et al. 2019

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Sensorimotor control studies have predominantly focused on how motor regions of the brain relay basic movement-related information such as position and velocity. However, motor control is often complex, involving the integration of sensory information, planning, visuomotor tracking, spatial mapping, retrieval and storage of memories, and may even be emotionally driven. This suggests that many more regions in the brain are involved beyond premotor and motor cortices. In this study, we exploited an experimental setup wherein activity from over 87 non-motor structures of the brain were recorded in eight human subjects executing a center-out motor task. The subjects were implanted with depth electrodes for clinical purposes. Using training data, we constructed subject-specific models that related spectral power of neural activity in six different frequency bands as well as a combined model containing the aggregation of multiple frequency bands to movement speed. We then tested the models by evaluating their ability to decode movement speed from neural activity in the test data set. The best models achieved a correlation of 0.38 ± 0.03 (mean ± standard deviation). Further, the decoded speeds matched the categorical representation of the test trials as correct or incorrect with an accuracy of 70 ± 2.75% across subjects. These models included features from regions such as the right hippocampus, left and right middle temporal gyrus, intraparietal sulcus, and left fusiform gyrus across multiple frequency bands. Perhaps more interestingly, we observed that the non-dominant hemisphere (ipsilateral to dominant hand) was most influential in decoding movement speed.

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Section: Methodsmentioning

confidence: 99%

Non-motor Brain Regions in Non-dominant Hemisphere Are Influential in Decoding Movement Speed

et al. 2019

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“…Decoding motor processes from non-primary motor areas is explored increasingly more. Recently, several studies demonstrated significant motor decoding results in humans in many different areas, such as the ventral premotor cortex [18], posterior parietal cortex [19][20][21], somatosensory cortex [18], supramarginal gyrus [18,20], temporal areas [22], insula [20,22] and hippocampus [20,22].…”

Section: Introductionmentioning

confidence: 99%

Executed and imagined grasping movements can be decoded from lower dimensional representation of distributed non-motor brain areas

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et al. 2022

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Using brain activity directly as input for assistive tool control can circumvent muscular dysfunction and increase functional independence for physically impaired people. Most invasive motor decoding studies focus on decoding neural signals from the primary motor cortex, which provides a rich but superficial and spatially local signal. Initial non-primary motor cortex decoding endeavors have used distributed recordings to demonstrate decoding of motor activity by grouping electrodes in mesoscale brain regions. While these studies show that there is relevant and decodable movement related information outside the primary motor cortex, these methods are still exclusionary to other mesoscale areas, and do not capture the full informational content of the motor system. In this work, we recorded intracranial EEG of 8 epilepsy patients, including all electrode contacts except those contacts in or adjacent to the central sulcus. We show that executed and imagined movements can be decoded from non-motor areas; combining all non-motor contacts into a lower dimensional representation provides enough information for a Riemannian decoder to reach an area under the curve of 0.83 ± 0.11. Additionally, by training our decoder on executed and testing on imagined movements, we demonstrate that between these two conditions there exists shared distributed information in the beta frequency range. By combining relevant information from all areas into a lower dimensional representation, the decoder was able to achieve high decoding results without information from the primary motor cortex. This representation makes the decoder more robust to perturbations, signal non-stationarities and neural tissue degradation. Our results indicate to look beyond the motor cortex and open up the way towards more robust and more versatile brain-computer interfaces.

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Neural Activity from Attention Networks Predicts Movement Errors

Cited by 2 publications

References 25 publications

Non-motor Brain Regions in Non-dominant Hemisphere Are Influential in Decoding Movement Speed

Non-motor Brain Regions in Non-dominant Hemisphere Are Influential in Decoding Movement Speed

Executed and imagined grasping movements can be decoded from lower dimensional representation of distributed non-motor brain areas

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