Prediction of Muscle Activities from Electrocorticograms in Primary Motor Cortex of Primates

Shin, Duk; Watanabe, Hidenori; Kambara, Hiroyuki; Nambu, Atsushi; Isa, Tadashi; Nishimura, Yukio; Koike, Yasuharu

doi:10.1371/journal.pone.0047992

Cited by 60 publications

(66 citation statements)

References 55 publications

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“…Within primary motor area, however, we could not find experimental evidences to explain the most effective site for force prediction according to anatomical knowledge. Our results just found that ECoG signals from the lateral areas and near areas of CS showed greater efficacy in prediction [33,36]. It might be needed the micro-sized ECoG electrode to find the most effective location within primary motor area.…”

Section: Discussionmentioning

confidence: 52%

“…Therefore, decoding muscle activities are key components for realizing neuro-prosthesis capable of the interaction with environments. We verified that ECoG signals are effective for predicting muscle activities in time varying series when performing sequential movements [36]. We used sparse linear regression to find the best fit between frequency bands of ECoG and electromyographic activity.…”

Section: Decoding Of Muscle Activities From Ecog Signalsmentioning

confidence: 78%

“…The neural data were down-sampled to 500 samples per second, and the motion data were up-sampled to 500 samples per second to match the neural data. The previous work showed the detail experimental information [36]. Experiment 2: Human data: All patients were seated upright on a chair at a table and were asked to perform the tasks using their left hands as shown in Figure 1B.…”

Section: Ethics Statementmentioning

confidence: 99%

“…Decoding method: ECoG signals were pre-processed with our previously proposed method [32,33,36]. Firstly, the signal data were rereferenced with a common average reference (CAR) and divided into A) Monkeys performed sequential right arm and hand movements, which consisted of reaching to a knob, grasping the knob with a lateral grip, pulling the knob closer, releasing the knob, and returning the hand to the home position, in a 3-D workspace [36].…”

Section: Ethics Statementmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 52%

Section: Decoding Of Muscle Activities From Ecog Signalsmentioning

confidence: 78%

Section: Ethics Statementmentioning

confidence: 99%