Decoding Single-Hand and Both-Hand Movement Directions From Noninvasive Neural Signals

Wang, Jiarong; Bi, Luzheng; Fei, Weijie; Guan, Cuntai

doi:10.1109/tbme.2020.3034112

Cited by 30 publications

(40 citation statements)

References 31 publications

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“…Effective feature representation is a crucial step for pattern classification due to the low signal-to-noise ratio of raw EEG signals [30]- [33]. The EEG signals contain multivariate information in space and time.…”

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confidence: 99%

STAnet: A Spatiotemporal Attention Network for Decoding Auditory Spatial Attention From EEG

Cai

Xie

et al. 2022

IEEE Trans. Biomed. Eng.

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Humans are able to localize the source of a sound. This enables them to direct attention to a particular speaker in a cocktail party. Psycho-acoustic studies show that the sensory cortices of the human brain respond to the location of sound sources differently, and the auditory attention itself is a dynamic and temporally based brain activity. In this work, we seek to build a computational model which uses both spatial and temporal information manifested in EEG signals for auditory spatial attention detection (ASAD). Methods: We propose an endto-end spatiotemporal attention network, denoted as STAnet, to detect auditory spatial attention from EEG. The STAnet is designed to assign differentiated weights dynamically to EEG channels through a spatial attention mechanism, and to temporal patterns in EEG signals through a temporal attention mechanism. Results: We report the ASAD experiments on two publicly available datasets. The STAnet outperforms other competitive models by a large margin under various experimental conditions. Its attention decision for 1-second decision window outperforms that of the state-ofthe-art techniques for 10-second decision window. Experimental results also demonstrate that the STAnet achieves competitive performance on EEG signals ranging from 64 to as few as 16 channels. Conclusion: This study provides evidence suggesting that efficient low-density EEG online decoding is within reach. Significance: This study also marks an important step towards the practical implementation of ASAD in real life applications.

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confidence: 99%

STAnet: A Spatiotemporal Attention Network for Decoding Auditory Spatial Attention From EEG

Cai

Xie

et al. 2022

IEEE Trans. Biomed. Eng.

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“…EEG signals were recorded by using a 64-electrode portable wireless EEG amplifier (NeuSen.W64, Neuracle, China), located at the following positions (according to the international 10–20 system): Cz, C1, C2, C3, C4, Fz, F3, F4, FCz, FC3, FC4, CP3, CP4, Oz, O1, O1, T7, T8, POz, Pz, P3, P4, P7, P8 (Wang et al, 2021 ), as shown in Figure 2 . The selected electrodes involved the frontal, central, parietal, and occipital regions, which were related to the cognition, motion, perception function.…”

Section: Methodsmentioning

confidence: 99%

“…Considering this issue, in 2020, Schwarz et al (2020) first used the low-frequency EEG features to discriminate unimanual and bimanual daily reach-and-grasp movement types and achieved a multi-class classification accuracy of 38.6% for a combination of one rest and six movement types. Furthermore, to put the single-hand and both-hand movement intention decoding from EEG signals into an active human augmentation system, in 2020, we investigated the neural signatures and classification of single-hand and both-hand movement directions, and the 6-class classification achieved a peak accuracy of 70.29% (Wang et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

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Using Non-linear Dynamics of EEG Signals to Classify Primary Hand Movement Intent Under Opposite Hand Movement

Wang

Fei

2022

Front. Neurorobot.

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Decoding human hand movement from electroencephalograms (EEG) signals is essential for developing an active human augmentation system. Although existing studies have contributed much to decoding single-hand movement direction from EEG signals, decoding primary hand movement direction under the opposite hand movement condition remains open. In this paper, we investigated the neural signatures of the primary hand movement direction from EEG signals under the opposite hand movement and developed a novel decoding method based on non-linear dynamics parameters of movement-related cortical potentials (MRCPs). Experimental results showed significant differences in MRCPs between hand movement directions under an opposite hand movement. Furthermore, the proposed method performed well with an average binary decoding accuracy of 89.48 ± 5.92% under the condition of the opposite hand movement. This study may lay a foundation for the future development of EEG-based human augmentation systems for upper limbs impaired patients and healthy people and open a new avenue to decode other hand movement parameters (e.g., velocity and position) from EEG signals.

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“…Traditionally, EEG decoding has relied on statistical methods that compute hand-crafted features such as common spatial pattern (CSP) 16 and employ classifiers such as support vector machines (SVM) to segragate those features 17 , 18 . Statistical EEG decoding methods suffer from two fundamental limitations that impede their use in accurate real-time applications 2 .…”

Section: Introductionmentioning

confidence: 99%

A neurophysiologically interpretable deep neural network predicts complex movement components from brain activity

Kumar

Michmizos

2022

Sci Rep

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The effective decoding of movement from non-invasive electroencephalography (EEG) is essential for informing several therapeutic interventions, from neurorehabilitation robots to neural prosthetics. Deep neural networks are most suitable for decoding real-time data but their use in EEG is hindered by the gross classes of motor tasks in the currently available datasets, which are solvable even with network architectures that do not require specialized design considerations. Moreover, the weak association with the underlying neurophysiology limits the generalizability of modern networks for EEG inference. Here, we present a neurophysiologically interpretable 3-dimensional convolutional neural network (3D-CNN) that captured the spatiotemporal dependencies in brain areas that get co-activated during movement. The 3D-CNN received topography-preserving EEG inputs, and predicted complex components of hand movements performed on a plane using a back-drivable rehabilitation robot, namely (a) the reaction time (RT) for responding to stimulus (slow or fast), (b) the mode of movement (active or passive, depending on whether there was an assistive force provided by the apparatus), and (c) the orthogonal directions of the movement (left, right, up, or down). We validated the 3D-CNN on a new dataset that we acquired from an in-house motor experiment, where it achieved average leave-one-subject-out test accuracies of 79.81%, 81.23%, and 82.00% for RT, active vs. passive, and direction classifications, respectively. Our proposed method outperformed the modern 2D-CNN architecture by a range of 1.1% to 6.74% depending on the classification task. Further, we identified the EEG sensors and time segments crucial to the classification decisions of the network, which aligned well with the current neurophysiological knowledge on brain activity in motor planning and execution tasks. Our results demonstrate the importance of biological relevance in networks for an accurate decoding of EEG, suggesting that the real-time classification of other complex brain activities may now be within our reach.

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Decoding Single-Hand and Both-Hand Movement Directions From Noninvasive Neural Signals

Cited by 30 publications

References 31 publications

STAnet: A Spatiotemporal Attention Network for Decoding Auditory Spatial Attention From EEG

STAnet: A Spatiotemporal Attention Network for Decoding Auditory Spatial Attention From EEG

Using Non-linear Dynamics of EEG Signals to Classify Primary Hand Movement Intent Under Opposite Hand Movement

A neurophysiologically interpretable deep neural network predicts complex movement components from brain activity

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