Spatio–Spectral Representation Learning for Electroencephalographic Gait-Pattern Classification

Goh, Sim Kuan; Abbass, Hussein A.; Tan, Kay Chen; Al‐Mamun, Abdullah; Thakor, Nitish V.; Bezerianos, Anastasios; Li, Junhua

doi:10.1109/tnsre.2018.2864119

Cited by 54 publications

(29 citation statements)

References 67 publications

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“…Sensor technologies with natural biological sensors enable humans to see, hear and feel things they could not see, hear or feel before. Robotic actuators allow humans to extend their body [8,9] when they lose an arm or a leg, or when they need extra strength, by using an exoskeleton to carry more weight than they could normally support. The technological landscape has evolved steadily from simple automation to advanced automation that can respond better than a human in a specific situation.…”

Section: Introductionmentioning

confidence: 99%

Social Integration of Artificial Intelligence: Functions, Automation Allocation Logic and Human-Autonomy Trust

Abbass

2019

Cogn Comput

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120

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Artificial intelligence (AI) is finding more uses in the human society resulting in a need to scrutinise the relationship between humans and AI. Technology itself has advanced from the mere encoding of human knowledge into a machine to designing machines that "know how" to autonomously acquire the knowledge they need, learn from it and act independently in the environment. Fortunately, this need is not new; it has scientific grounds that could be traced back to the inception of computers. This paper uses a multidisciplinary lens to explore how the natural cognitive intelligence in a human could interface with the artificial cognitive intelligence of a machine. The scientific journey over the last 50 years will be examined to understand the Human-AI relationship, and to present the nature of, and the role of trust in, this relationship. Risks and opportunities sitting at the human-AI interface will be studied to reveal some of the fundamental technical challenges for a trustworthy human-AI relationship. The critical assessment of the literature leads to the conclusion that any social integration of AI into the human social system would necessitate a form of a relationship on one level or another in society, meaning that humans will "always" actively participate in certain decision-making loops-either in-the-loop or on-the-loop-that will influence the operations of AI, regardless of how sophisticated it is.

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Section: Introductionmentioning

confidence: 99%

Social Integration of Artificial Intelligence: Functions, Automation Allocation Logic and Human-Autonomy Trust

Abbass

2019

Cogn Comput

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120

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“…Nevertheless, only a small amount of studies has been conducted on how the brain mediates different complex gait movements such as running and walking [26]. As there is less work on the complexity of shifting from sitting to standing and vice versa [27], the current study aimed to combine action observation (AO) and MI as a potential rehabilitation strategy for lower limbs dysfunction.…”

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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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“…EEG recording in stroke, where the pathology is at the brain level, has been problematic when compared to other neurological pathologies such as spinal cord injury (Castermans et al, 2014 ), and similarly EMG recording on the stroke-affected side can be problematic (Sarasola-Sanz et al, 2017 ; Li et al, 2018 ). Uncertainty still exists in the literature on the best choice of EEG metric (Goh et al, 2018 ) and in the ability of EMG to respond accurately in real time (Fan and Yin, 2013 ). This review, summarizing the current state of the art in neural interface during robotic-assisted gait training after a stroke, identifies a lack of standardization in data collection in this field and provides guidance for study design and reporting future studies.…”

Section: Discussionmentioning

confidence: 99%

A Systematic Review Establishing the Current State-of-the-Art, the Limitations, and the DESIRED Checklist in Studies of Direct Neural Interfacing With Robotic Gait Devices in Stroke Rehabilitation

et al. 2020

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Background: Stroke is a disease with a high associated disability burden. Robotic-assisted gait training offers an opportunity for the practice intensity levels associated with good functional walking outcomes in this population. Neural interfacing technology, electroencephalography (EEG), or electromyography (EMG) can offer new strategies for robotic gait re-education after a stroke by promoting more active engagement in movement intent and/or neurophysiological feedback. Objectives: This study identifies the current state-of-the-art and the limitations in direct neural interfacing with robotic gait devices in stroke rehabilitation. Methods: A pre-registered systematic review was conducted using standardized search operators that included the presence of stroke and robotic gait training and neural biosignals (EMG and/or EEG) and was not limited by study type. Results: From a total of 8,899 papers identified, 13 articles were considered for the final selection. Only five of the 13 studies received a strong or moderate quality rating as a clinical study. Three studies recorded EEG activity during robotic gait, two of which used EEG for BCI purposes. While demonstrating utility for decoding kinematic and EMG-related gait data, no EEG study has been identified to close the loop between robot and human. Twelve of the studies recorded EMG activity during or after robotic walking, primarily as an outcome measure. One study used multisource information fusion from EMG, joint angle, and force to modify robotic commands in real time, with higher error rates observed during active movement. A novel study identified used EMG data during robotic gait to derive the optimal, individualized robot-driven step trajectory. Lennon et al. Stroke: Neural Interfaced Robotic Walking Conclusions: Wide heterogeneity in the reporting and the purpose of neurobiosignal use during robotic gait training after a stroke exists. Neural interfacing with robotic gait after a stroke demonstrates promise as a future field of study. However, as a nascent area, direct neural interfacing with robotic gait after a stroke would benefit from a more standardized protocol for biosignal collection and processing and for robotic deployment. Appropriate reporting for clinical studies of this nature is also required with respect to the study type and the participants' characteristics.

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Spatio–Spectral Representation Learning for Electroencephalographic Gait-Pattern Classification

Cited by 54 publications

References 67 publications

Social Integration of Artificial Intelligence: Functions, Automation Allocation Logic and Human-Autonomy Trust

Social Integration of Artificial Intelligence: Functions, Automation Allocation Logic and Human-Autonomy Trust

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

A Systematic Review Establishing the Current State-of-the-Art, the Limitations, and the DESIRED Checklist in Studies of Direct Neural Interfacing With Robotic Gait Devices in Stroke Rehabilitation

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