Decoding EEG Rhythms During Action Observation, Motor Imagery, and Execution for Standing and Sitting

Chaisaen, Rattanaphon; Autthasan, Phairot; Mingchinda, Nopparada; Leelaarporn, Pitshaporn; Kunaseth, Narin; Tammajarung, Suppakorn; Manoonpong, Poramate; Mukhopadhyay, Subhas Chandra; Wilaiprasitporn, Theerawit

doi:10.1109/jsen.2020.3005968

Cited by 71 publications

(36 citation statements)

References 52 publications

(53 reference statements)

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“…For this reason, brain computer interface (BCI) technologies based on non-invasive Electroencephalography (EEG) have been introduced to decode movement intention from brain electrical signals, even in absence of any muscular activity. Previous works have proven the feasibility of predicting motion intention with BCI to detect sit-to-stand and stand-to-sit movement (Chaisaen et al, 2020 ), to trigger lower limb exoskeletons (Kilicarslan et al, 2013 ; Lee et al, 2017 ), but also to decode walking patterns from EEG signals (Presacco et al, 2011 ; Nakagome et al, 2020 ; Tortora et al, 2020 ). However, interfaces based on EEG signals alone are not reliable enough for most clinical applications and to control advanced neurorobotics devices yet due to their low reliability, low accuracy and low informative content (Vaughan et al, 1996 ; Wolpaw et al, 2000 ).…”

Section: Introductionmentioning

confidence: 99%

Hybrid Human-Machine Interface for Gait Decoding Through Bayesian Fusion of EEG and EMG Classifiers

et al. 2020

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Despite the advances in the field of brain computer interfaces (BCI), the use of the sole electroencephalography (EEG) signal to control walking rehabilitation devices is currently not viable in clinical settings, due to its unreliability. Hybrid interfaces (hHMIs) represent a very recent solution to enhance the performance of single-signal approaches. These are classification approaches that combine multiple human-machine interfaces, normally including at least one BCI with other biosignals, such as the electromyography (EMG). However, their use for the decoding of gait activity is still limited. In this work, we propose and evaluate a hybrid human-machine interface (hHMI) to decode walking phases of both legs from the Bayesian fusion of EEG and EMG signals. The proposed hHMI significantly outperforms its single-signal counterparts, by providing high and stable performance even when the reliability of the muscular activity is compromised temporarily (e.g., fatigue) or permanently (e.g., weakness). Indeed, the hybrid approach shows a smooth degradation of classification performance after temporary EMG alteration, with more than 75% of accuracy at 30% of EMG amplitude, with respect to the EMG classifier whose performance decreases below 60% of accuracy. Moreover, the fusion of EEG and EMG information helps keeping a stable recognition rate of each gait phase of more than 80% independently on the permanent level of EMG degradation. From our study and findings from the literature, we suggest that the use of hybrid interfaces may be the key to enhance the usability of technologies restoring or assisting the locomotion on a wider population of patients in clinical applications and outside the laboratory environment.

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Section: Introductionmentioning

confidence: 99%

Hybrid Human-Machine Interface for Gait Decoding Through Bayesian Fusion of EEG and EMG Classifiers

et al. 2020

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“…A channel selection is peformed manually by selecting only three electrodes C 3 , C z and C 4 allowing to reach a mean classification accuracy beyond 71% and 70% using, respectively, the CNN-FC and the CSP with SVM algorithms. In [22], Rattanaphon et al, investigated the role of action observation and MI during the standing and sitting tasks. In this study, a fixed time window of 2s with an overlapping factor of 0.2s and 9 filter banks are used to track EEG rhythms during the sit-to-stand and stand-to-sit transitions.…”

Section: Figure 1: Typical Eeg Signal Processing Chainmentioning

confidence: 99%

An Effective Zeros-Time Windowing Strategy to Detect Sensorimotor Rhythms Related to Motor Imagery EEG Signals

2020

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Brain-computer interface (BCI) acquires, analyzes and transforms human brain activity to control commands allowing as such disabled people to communicate or control external devices. A motor imagery-based BCI enables patients to control artificial peripherals and communicate with the outside world by merely thinking of the task such as, e.g., the imagination of left-hand, right-hand, or foot movement. The mere intention of moving one of the limbs triggers neural activity, which is induced in the primary sensorimotor areas like that observed with real executed movements. Tracking generated sensorimotor rhythms (SMRs) and extracting robust and informative features from electroencephalogram (EEG) signals are challenging due to the time-varying nature of EEG signals and the inter-human variability. In this paper, we proposed an EEG-zeros-time windowing (E2ZTW) approach based on a highly decaying window function to track SMRs and identify the temporal epochs containing useful information without any prior information on the trigger. The proposed approach involves the application of the group-delay function, allowing the improvement of the spectral resolution due to the additive property of the function on individual resonances. Some algorithms were integrated into the proposed approach, such as the common spatial pattern algorithm, which is used to extract features and linear discriminant analysis and a convolutional neural network, which are used for the classification of the features. The effectiveness of the proposed approach in tracking the SMRs rhythms is evaluated in terms of accuracy. Experiments were performed on three public datasets provided by BCI competition for 17 subjects. Following experimental results, it is shown that discrimination between the left-and right-hand movements can be achieved within a few seconds with high classification accuracy. As compared to other state-of-art techniques, the proposed approach achieves an average classification accuracy and standard error values of 82% and 13, respectively, thereby outperforming existing algorithm by an accuracy mean of 2%.

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“…Previous studies showed that MI and ME for hand and foot movements activate comparable brain areas, also for the execution of swallowing [23] . MI movement pattern discrimination were based on quantification of event-related synchronization/desynchronization (ERS/ERD) using bandpower (BP) [12] , [24] , which were cortical rhythms characterized by the mu and beta neural activity patterns [25] . ERD showed stronger contra-lateralization features with movement intention and execution in the sensorimotor cortices, while ERS was found prominently in the ipsilateral hemisphere [25] .…”

Section: Introductionmentioning

confidence: 99%

“…A previous study using a simple finger-tapping task suggested that the increasing of oxyHb levels in the supplemental motor area (SMA) and premotor area (PMA) during MI were similar to those observed during ME [31] . Chaisaen et al [25] presented the decoding EEG rhythms during action observation, MI, and ME for the actions of standing and sitting.…”

Section: Introductionmentioning

confidence: 99%

Massage Therapy’s Effectiveness on the Decoding EEG Rhythms of Left/Right Motor Imagery and Motion Execution in Patients With Skeletal Muscle Pain

Fan

et al. 2021

IEEE J. Transl. Eng. Health Med.

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Objective: Most of effectiveness assessments of the widely-used Massage therapy were based on subjective routine clinical assessment tools, such as Visual Analogue Scale (VAS) score. However, few studies demonstrated the impact of massage on the Electroencephalograph (EEG) rhythm decoding of Motor imagery (MI) and motion execution (ME) with trunk left/right bending in patients with skeletal muscle pain. Method: We used the sample entropy (SampEn), permutation entropy (PermuEn), common spatial pattern (CSP) features, support vector machine (SVM) and logic regression (LR) classifiers. We also used the convolutional neural network (CNN) and attention-based bi-directional long short-term memory (BiLSTM) for classification. Results: The averaged SampEn and PermuEn values of alpha rhythm decreased in almost fourteen channels for five statuses (quiet, MI with left/right bending, ME with left/right bending). It indicated that massage alleviates the pain for the patients of skeletal pain. Furthermore, compared with the SVM and LR classifiers, the BiLSTM method achieved a better area under curve (AUC) of 0.89 for the classification of MI with trunk left/right bending before massage. The AUC became smaller after massage than that before massage for the classification of MI with trunk left/right bending using CNN and BiLSTM methods. The Permutation direct indicator (PDI) score showed the significant difference for patients in different statuses (before vs after massage, and MI vs ME). Conclusions: Massage not only affects the quiet status, but also affects the MI and ME. Clinical Impact: Massage therapy may affect a bit on the accuracy of MI with trunk left/right bending and it change the topography of MI and ME with trunk left/right bending for the patients with skeletal muscle pain.

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Decoding EEG Rhythms During Action Observation, Motor Imagery, and Execution for Standing and Sitting

Cited by 71 publications

References 52 publications

Hybrid Human-Machine Interface for Gait Decoding Through Bayesian Fusion of EEG and EMG Classifiers

Hybrid Human-Machine Interface for Gait Decoding Through Bayesian Fusion of EEG and EMG Classifiers

An Effective Zeros-Time Windowing Strategy to Detect Sensorimotor Rhythms Related to Motor Imagery EEG Signals

Massage Therapy’s Effectiveness on the Decoding EEG Rhythms of Left/Right Motor Imagery and Motion Execution in Patients With Skeletal Muscle Pain

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