Brain–Computer Interface after Nervous System Injury

Burns, Alexis; Adeli, Hojjat; Buford, John A.

doi:10.1177/1073858414549015

Cited by 96 publications

(32 citation statements)

References 80 publications

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“…Our classification accuracy was a bit (<2%) lower than some of the previous similar studies, 35 large because we tested two different MVCs, which could have induced most of the errors. Brain-computer interface [36][37][38][39][40] has been an active research area for the control of robotic devices, through the decoding of neural control signals directly from electroencephalogram (EEG) or individual cortical neuron discharge activities. Our myoelectric-based control approach can be complementary to these approaches for neural-machine interface.…”

Section: Pattern Recognitionmentioning

confidence: 99%

Extracting and Classifying Spatial Muscle Activation Patterns in Forearm Flexor Muscles Using High-Density Electromyogram Recordings

Dai

2019

Int. J. Neur. Syst.

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The human hand is capable of producing versatile yet precise movements largely owing to the complex neuromuscular systems that control our finger movement. This study seeks to quantify the spatial activation patterns of the forearm flexor muscles during individualized finger flexions. High-density (HD) surface electromyogram (sEMG) signals of forearm flexor muscles were obtained, and individual motor units were decomposed from the sEMG. Both macro-level spatial patterns of EMG activity and micro-level motor unit distributions were used to systematically characterize the forearm flexor activation patterns. Different features capturing the spatial patterns were extracted, and the unique patterns of forearm flexor activation were then quantified using pattern recognition approaches. We found that the forearm flexor spatial activation during the ring finger flexion was mostly distinct from other fingers, whereas the activation patterns of the middle finger were least distinguishable. However, all the different activation patterns can still be classified in high accuracy (94-100%) using pattern recognition. Our findings indicate that the partial overlapping of neural activation can limit accurate identification of specific finger movement based on limited recordings and sEMG features, and that HD sEMG recordings capturing detailed spatial activation patterns at both macro- and micro-levels are needed.

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Section: Pattern Recognitionmentioning

confidence: 99%

Extracting and Classifying Spatial Muscle Activation Patterns in Forearm Flexor Muscles Using High-Density Electromyogram Recordings

Dai

2019

Int. J. Neur. Syst.

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“…Brain–computer interface (BCI) systems provide a communication and control path between a computer and a human with applications in diverse areas such as medical (Burns et al, ; Yin et al, ; Sereshkeh et al, ), robotics (Lu et al, ; Chen et al, ; Sabri et al, ), the military, and games. Development of BCI systems requires a multidisciplinary approach crossing many fields including neurobiology, psychology, engineering, mathematics, and computer science that is highly affected by developments in each of those fields (Mirzaei et al, ; Mirzaei and Adeli, ; Jiao et al, ).…”

Section: Introductionmentioning

confidence: 99%

A novel end‐to‐end deep learning scheme for classifying multi‐class motor imagery electroencephalography signals

et al. 2019

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An important subfield of brain–computer interface is the classification of motor imagery (MI) signals where a presumed action, for example, imagining the hands' motions, is mentally simulated. The brain dynamics of MI is usually measured by electroencephalography (EEG) due to its noninvasiveness. The next generation of brain–computer interface systems can benefit from the generative deep learning (GDL) models by providing end‐to‐end (e2e) machine learning and increasing their accuracy. In this study, to exploit the e2e‐property of deep learning models, a novel GDL methodology is proposed where only minimal objective‐free preprocessing steps are needed. Furthermore, to deal with the complicated multi‐class MI–EEG signals, an innovative multilevel GDL‐based classifying scheme is proposed. The effectiveness of the proposed model and its robustness against noisy MI–EEG signals is evaluated using two different GDL models, that is, deep belief network and stacked sparse autoencoder in e2e manner. Experimental results demonstrate the effectiveness of the proposed methodology with improved accuracy compared with the widely used filter bank common spatial patterns algorithm.

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“…Although, BrainComputer interface (BCI) is usually performed using electroencephalogram (EEG), 13,14 it is possible to use EMG decomposition for human-machine interfacing to control external devices. …”

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confidence: 99%

A Real-Time Method for Decoding the Neural Drive to Muscles Using Single-Channel Intra-Muscular EMG Recordings

Karimimehr

Marateb

Muceli

et al. 2017

Int. J. Neur. Syst.

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The neural command from motor neurons to muscles -sometimes referred to as the neural drive to muscle -can be identified by decomposition of electromyographic (EMG) paper proposes for the first time an innovative method for fully automatic and real-time intramuscular EMG (iEMG) decomposition. The method is based on online single-pass density-based clustering and adaptive classification of bivariate features, using the concept of potential measure. No attempt was made to resolve superimposed motor unit action potentials. The proposed algorithm was validated on sets of simulated and experimental iEMG signals. Signals were recorded from the biceps femoris long-head, vastus medialis and lateralis and tibialis anterior muscles during low-to-moderate isometric constant-force and linearly-varying force contractions. The average number of missed, duplicated and erroneous clusters for the examined signals was 0.5 ± 0.8, 1.2 ± 1.0, and 1.0 ± 0.8, respectively. The average decomposition accuracy (defined similar to signal detection theory but without using True Negatives in the denominator) and coefficient of determination (variance accounted for) for the cumulative discharge rate estimation were 70 ± 9%, and 94 ± 5%, respectively. The time cost for processing each 200 ms iEMG interval was 43 ± 16 (21-97) ms. However, computational time generally increases over time as a function of frames/signal epochs. Meanwhile, the incremental accuracy defined as the accuracy of real-time analysis of each signal epoch, was 74 ± 18% for epochs recorded after initial one second. The proposed algorithm is thus a promising new tool for neural decoding in the next-generation of prosthetic control.

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Brain–Computer Interface after Nervous System Injury

Cited by 96 publications

References 80 publications

Extracting and Classifying Spatial Muscle Activation Patterns in Forearm Flexor Muscles Using High-Density Electromyogram Recordings

Extracting and Classifying Spatial Muscle Activation Patterns in Forearm Flexor Muscles Using High-Density Electromyogram Recordings

A novel end‐to‐end deep learning scheme for classifying multi‐class motor imagery electroencephalography signals

A Real-Time Method for Decoding the Neural Drive to Muscles Using Single-Channel Intra-Muscular EMG Recordings

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