EEG-Based Lower-Limb Movement Onset Decoding: Continuous Classification and Asynchronous Detection

Liu, Dong; Chen, Weihai; Lee, Kyuhwa; Chavarriaga, Ricardo; Iwane, Fumiaki; Bouri, Mohamed; Pei, Zhongcai; Millán, José del R.

doi:10.1109/tnsre.2018.2855053

Cited by 61 publications

(52 citation statements)

References 39 publications

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“…The separation of TPR and FP/min from the classification accuracy allows evaluation of the movement detection performance separately from the movement classification. The detection of movements from MRCPs, particularly lower limb movements, is well reported in the literature 30,44,62–65,67,80 . Lower-limb MRCPs-based movement detection performances up to a TPR of 82.5% with FP/min of 1.38 are reported 62 , which are nearly 3 times better than the performance reached in this work.…”

Section: Discussionmentioning

confidence: 78%

Attempted Arm and Hand Movements can be Decoded from Low-Frequency EEG from Persons with Spinal Cord Injury

Ofner

Schwarz

Pereira

et al. 2019

Sci Rep

119

108

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We show that persons with spinal cord injury (SCI) retain decodable neural correlates of attempted arm and hand movements. We investigated hand open, palmar grasp, lateral grasp, pronation, and supination in 10 persons with cervical SCI. Discriminative movement information was provided by the time-domain of low-frequency electroencephalography (EEG) signals. Based on these signals, we obtained a maximum average classification accuracy of 45% (chance level was 20%) with respect to the five investigated classes. Pattern analysis indicates central motor areas as the origin of the discriminative signals. Furthermore, we introduce a proof-of-concept to classify movement attempts online in a closed loop, and tested it on a person with cervical SCI. We achieved here a modest classification performance of 68.4% with respect to palmar grasp vs hand open (chance level 50%).

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Section: Discussionmentioning

confidence: 78%

Attempted Arm and Hand Movements can be Decoded from Low-Frequency EEG from Persons with Spinal Cord Injury

Ofner

Schwarz

Pereira

et al. 2019

Sci Rep

119

108

View full text Add to dashboard Cite

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“…Movement-related Cortical Potentials: A threshold-based method generally plays a significant role in extracting the actual movement onset detected by the EMG [37]. In this study, we employed the threshold-based method to determine the actual movement onset of each sit-to-stand/stand-to-sit transition.…”

Section: Eeg Pre-processingmentioning

confidence: 99%

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

et al. 2020

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Event-related desynchronization and synchronization (ERD/S) and movement-related cortical potential (MRCP) play an important role in brain-computer interfaces (BCI) for lower limb rehabilitation, particularly in standing and sitting. However, little is known about the differences in the cortical activation between standing and sitting, especially how the brain's intention modulates the pre-movement sensorimotor rhythm as they do for switching movements. In this study, we aim to investigate the decoding of continuous EEG rhythms during action observation (AO), motor imagery (MI), and motor execution (ME) for the actions of standing and sitting. We developed a behavioral task in which participants were instructed to perform both AO and MI/ME in regard to the transitioning actions of sit-to-stand and stand-to-sit. Our results demonstrated that the ERD was prominent during AO, whereas ERS was typical during MI at the alpha band across the sensorimotor area. A combination of the filter bank common spatial pattern (FBCSP) and support vector machine (SVM) for classification was used for both offline and classifier testing analyses. The offline analysis indicated the classification of AO and MI providing the highest mean accuracy at 82.73±2.54% in the stand-to-sit transition. By applying the classifier testing analysis, we demonstrated the higher performance of decoding neural intentions from the MI paradigm in comparison to the ME paradigm. These observations led us to the promising aspect of using our developed tasks based on the integration of both AO and MI to build future exoskeleton-based rehabilitation systems.

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“…In both preliminary optimization analysis and real-time tests, the results obtained are very similar. Liu et al (2018) This research aims to decode the plantar flexion movement intention using continuous classification and asynchronous detection in a gait training paradigm for self-paced gait using movement-related cortical potentials.…”

Section: Fnirs Signalsmentioning

confidence: 99%

“…The results show that the removal of ocular artifacts improves performance in comparison to using individual modality (Fatourechi et al, 2007). Liu et al (2018) used three EOG electrodes below outer canthi and above nasion to record the eye movement while using a regression-based approach to remove EOG artifacts. Similarly, in another study, EOG signals were recorded from two pairs of bipolar electrodes to detect vertical and horizontal eye movements, and then artifacts were removed using a regression algorithm (Li et al, 2016).…”

Section: Fusion Eeg and Fnirs With Other Bio-signalsmentioning

confidence: 99%

Analysis of Human Gait Using Hybrid EEG-fNIRS-Based BCI System: A Review

Khan

Naseer

Yazidi

et al. 2021

Front. Hum. Neurosci.

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Human gait is a complex activity that requires high coordination between the central nervous system, the limb, and the musculoskeletal system. More research is needed to understand the latter coordination's complexity in designing better and more effective rehabilitation strategies for gait disorders. Electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) are among the most used technologies for monitoring brain activities due to portability, non-invasiveness, and relatively low cost compared to others. Fusing EEG and fNIRS is a well-known and established methodology proven to enhance brain–computer interface (BCI) performance in terms of classification accuracy, number of control commands, and response time. Although there has been significant research exploring hybrid BCI (hBCI) involving both EEG and fNIRS for different types of tasks and human activities, human gait remains still underinvestigated. In this article, we aim to shed light on the recent development in the analysis of human gait using a hybrid EEG-fNIRS-based BCI system. The current review has followed guidelines of preferred reporting items for systematic reviews and meta-Analyses (PRISMA) during the data collection and selection phase. In this review, we put a particular focus on the commonly used signal processing and machine learning algorithms, as well as survey the potential applications of gait analysis. We distill some of the critical findings of this survey as follows. First, hardware specifications and experimental paradigms should be carefully considered because of their direct impact on the quality of gait assessment. Second, since both modalities, EEG and fNIRS, are sensitive to motion artifacts, instrumental, and physiological noises, there is a quest for more robust and sophisticated signal processing algorithms. Third, hybrid temporal and spatial features, obtained by virtue of fusing EEG and fNIRS and associated with cortical activation, can help better identify the correlation between brain activation and gait. In conclusion, hBCI (EEG + fNIRS) system is not yet much explored for the lower limb due to its complexity compared to the higher limb. Existing BCI systems for gait monitoring tend to only focus on one modality. We foresee a vast potential in adopting hBCI in gait analysis. Imminent technical breakthroughs are expected using hybrid EEG-fNIRS-based BCI for gait to control assistive devices and Monitor neuro-plasticity in neuro-rehabilitation. However, although those hybrid systems perform well in a controlled experimental environment when it comes to adopting them as a certified medical device in real-life clinical applications, there is still a long way to go.

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EEG-Based Lower-Limb Movement Onset Decoding: Continuous Classification and Asynchronous Detection

Cited by 61 publications

References 39 publications

Attempted Arm and Hand Movements can be Decoded from Low-Frequency EEG from Persons with Spinal Cord Injury

Attempted Arm and Hand Movements can be Decoded from Low-Frequency EEG from Persons with Spinal Cord Injury

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

Analysis of Human Gait Using Hybrid EEG-fNIRS-Based BCI System: A Review

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