High gamma power in ECoG reflects cortical electrical stimulation effects on unit activity in layers V/VI

Yazdan-Shahmorad, Azadeh; Kipke, Daryl R.; Lehmkuhle, Mark J.

doi:10.1088/1741-2560/10/6/066002

Cited by 23 publications

(22 citation statements)

References 48 publications

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“…The exact limits of this band are unknown, with previous studies successfully utilizing frequencies as low as 50 Hz and as high as 100, 200, or even 300 Hz. Nonetheless, this finding directly aligns with previous research studies, which suggest that highfrequency activity corresponds most with the firing of individual neurons within a population [76][77][78]. On the other hand, covert neural speech decoding, which may be necessary for patients with less-severe cases of LIS, has proven extremely challenging, largely due to weaker activation observed during covert versus perceived and overt speech and the lack of a clear response to align to as with overt speech, e.g., acoustic or articulatory recordings.…”

Section: Feasibility Of Speech Bcisupporting

confidence: 91%

The Potential for a Speech Brain–Computer Interface Using Chronic Electrocorticography

2019

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A brain-computer interface (BCI) is a technology that uses neural features to restore or augment the capabilities of its user. A BCI for speech would enable communication in real time via neural correlates of attempted or imagined speech. Such a technology would potentially restore communication and improve quality of life for locked-in patients and other patients with severe communication disorders. There have been many recent developments in neural decoders, neural feature extraction, and brain recording modalities facilitating BCI for the control of prosthetics and in automatic speech recognition (ASR). Indeed, ASR and related fields have developed significantly over the past years, and many lend many insights into the requirements, goals, and strategies for speech BCI. Neural speech decoding is a comparatively new field but has shown much promise with recent studies demonstrating semantic, auditory, and articulatory decoding using electrocorticography (ECoG) and other neural recording modalities. Because the neural representations for speech and language are widely distributed over cortical regions spanning the frontal, parietal, and temporal lobes, the mesoscopic scale of population activity captured by ECoG surface electrode arrays may have distinct advantages for speech BCI, in contrast to the advantages of microelectrode arrays for upper-limb BCI. Nevertheless, there remain many challenges for the translation of speech BCIs to clinical populations. This review discusses and outlines the current stateof-the-art for speech BCI and explores what a speech BCI using chronic ECoG might entail.

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Section: Feasibility Of Speech Bcisupporting

confidence: 91%

The Potential for a Speech Brain–Computer Interface Using Chronic Electrocorticography

2019

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“…This result corresponds to previous studies as well [27,[41][42][43][44][45]. Especially, the γ band was most effective than any other bands because γ band activity of ECoG signals reflect the unit activity in layers V/VI in primary motor area [46].…”

Section: Most Effective Frequency Band For Decodingsupporting

confidence: 91%

Decoding of Kinetic and Kinematic Information from Electrocorticograms in Sensorimotor Cortex: A Review

Nakanishi¹

2014

Int J Neurorehabilitation Eng

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IntroductionBrain-machine interfaces (BMI) are useful technologies to provide assistance to disabled individuals, allowing them interaction with their environments. A number of prominent brain-machine interface studies have arisen over the past two decades. These BMI systems translate brain signals into commands for controlling devices such as cursors [1], spelling devices [2], and neural prosthetics [3][4][5][6][7][8][9]. This new communication has not only the potential to help to disabled persons but also provide insight into the motor system of the brain [10][11][12][13][14].Several sensors have been developed to measure brain signals. These are mainly categorized into two types, invasive sensor i.e. intracortical microelectrodes and non-invasive sensors such as electroencephalography (EEG) and magneto encephalography. Lots of invasive BMI studies have successfully demonstrated prosthetic devices [6][7][8][9]. However, they have the risk such as brain injury. Since EEG are non-invasive and have high temporal resolution, previous works have developed such as online cursor control [15], direction of hand movements [16,17], a spelling device [18], and neuro feedback for rehabilitation [19,20]. Although a large number of these non-invasive works succeeded in classification of movement intention, prediction of time-varying trajectories is difficult due to insufficient spatial resolution and low signal-to-noise ratio in such methods.Electrocorticography (ECoG) is an alternative approach to less invasive BMIs [21][22][23][24][25][26][27][28][29]. ECoG is a technique that measures electrical activity in the cerebral cortex by means of electrodes placed directly on the surface of the brain. Compared to EEG, ECoG has higher spatio-temporal resolution with better signal-to-noise ratio than scalp EEG [30,31]. ECoG has also shown potential as a stable longterm recording method [27]. Several studies using ECoG have already succeeded in the classification of movement direction [22,23], grasp type [28], and prediction of hand trajectory [24,26,27], and decoding of hand trajectories [25,27,32], arm trajectories [33] and finger movement [34,35]. Predictions of muscle activities from ECoG signals during reaching and grasping movements in monkeys have also been successful [36]. Despite these successes, however, there still remains considerable work for the realization of ECoG-based neuroprosthesis. Since the human neuromuscular system naturally modulates mechanical stiffness and viscosity to achieve proper interaction with the environment, we have not only decoded kinematic information such as trajectory but also kinetic information such as torque, stiffness AbstractBrain-machine interface techniques have been applied in a number of studies to control neuromotor prostheses and for neuro-rehabilitation in the hopes of providing a means to restore lost motor function. Electrocorticography has seen recent use in this regard because it offers a higher spatiotemporal resolution than non-invasive electroencephalography and is less in...

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“…9 A, B). HGP in cortex has been believed to track the level of local spiking and correlates with functional MRI signal (Ojemann et al, 2013;Yazdan-Shahmorad et al, 2013;Watrous et al, 2015). Our dissociation of these signals in NBM suggests that the spiking-HGP link is not universal.…”

Section: Spike-lfp Dissociationmentioning

confidence: 72%

“…We compared the relative roles of the NBM and dorsolateral prefrontal cortex (dlPFC) in learning by recording neural activity in nonhuman primates (NHPs) performing an associative learning task. We chose the dlPFC because this region is strongly implicated in integrating cognitive functions such as learning, attention, error prediction, and decision making (Wallis and Miller, 2003;Tsujimoto and Sawaguchi, 2005;Ichihara-Takeda and Funahashi, 2008;Asaad and Eskandar, 2011;Kahnt et al, 2011). In addition, neurons in the NBM have been shown to project to the macaque dlPFC, specifically the principal sulcus (Mesulam et al, 1983).…”

Section: Introductionmentioning

confidence: 99%

Multimodal Encoding of Novelty, Reward, and Learning in the Primate Nucleus Basalis of Meynert

et al. 2018

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Associative learning is crucial for daily function, involving a complex network of brain regions. One region, the nucleus basalis of Meynert (NBM), is a highly interconnected, largely cholinergic structure implicated in multiple aspects of learning. We show that single neurons in the NBM of nonhuman primates (NHPs; = 2 males;) encode learning a new association through spike rate modulation. However, the power of low-frequency local field potential (LFP) oscillations decreases in response to novel, not-yet-learned stimuli but then increase as learning progresses. Both NBM and the dorsolateral prefrontal cortex encode confidence in novel associations by increasing low- and high-frequency LFP power in anticipation of expected rewards. Finally, NBM high-frequency power dynamics are anticorrelated with spike rate modulations. Therefore, novelty, learning, and reward anticipation are separately encoded through differentiable NBM signals. By signaling both the need to learn and confidence in newly acquired associations, NBM may play a key role in coordinating cortical activity throughout the learning process. Degradation of cells in a key brain region, the nucleus basalis of Meynert (NBM), correlates with Alzheimer's disease and Parkinson's disease progression. To better understand the role of this brain structure in learning and memory, we examined neural activity in the NBM in behaving nonhuman primates while they performed a learning and memory task. We found that single neurons in NBM encoded both salience and an early learning, or cognitive state, whereas populations of neurons in the NBM and prefrontal cortex encode learned state and reward anticipation. The NBM may thus encode multiple stages of learning. These multimodal signals might be leveraged in future studies to develop neural stimulation to facilitate different stages of learning and memory.

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High gamma power in ECoG reflects cortical electrical stimulation effects on unit activity in layers V/VI

Cited by 23 publications

References 48 publications

The Potential for a Speech Brain–Computer Interface Using Chronic Electrocorticography

The Potential for a Speech Brain–Computer Interface Using Chronic Electrocorticography

Decoding of Kinetic and Kinematic Information from Electrocorticograms in Sensorimotor Cortex: A Review

Multimodal Encoding of Novelty, Reward, and Learning in the Primate Nucleus Basalis of Meynert

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