EEG source space analysis of the supervised factor analytic approach for the classification of multi-directional arm movement

Handiru, Vikram Shenoy; Vinod, A. P.; Guan, Cuntai

doi:10.1088/1741-2552/aa6baf

Cited by 24 publications

(16 citation statements)

References 62 publications

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“…We used a standardized low-resolution electromagnetic tomography (sLORETA)-based current density estimation technique for inverse modeling the brain points that were activated from the EEG signals. sLORETA is a variant of the weighted minimum norm estimation technique for obtaining an inverse solution [ 31 , 58 ]. We visualized the activated regions of the brain for each task and showed them in terms of the horizontal, sagittal, and coronal planes, as shown in Fig.…”

Section: Data Validationmentioning

confidence: 99%

Multimodal signal dataset for 11 intuitive movement tasks from single upper extremity during multiple recording sessions

et al. 2020

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Background Non-invasive brain–computer interfaces (BCIs) have been developed for realizing natural bi-directional interaction between users and external robotic systems. However, the communication between users and BCI systems through artificial matching is a critical issue. Recently, BCIs have been developed to adopt intuitive decoding, which is the key to solving several problems such as a small number of classes and manually matching BCI commands with device control. Unfortunately, the advances in this area have been slow owing to the lack of large and uniform datasets. This study provides a large intuitive dataset for 11 different upper extremity movement tasks obtained during multiple recording sessions. The dataset includes 60-channel electroencephalography, 7-channel electromyography, and 4-channel electro-oculography of 25 healthy participants collected over 3-day sessions for a total of 82,500 trials across all the participants. Findings We validated our dataset via neurophysiological analysis. We observed clear sensorimotor de-/activation and spatial distribution related to real-movement and motor imagery, respectively. Furthermore, we demonstrated the consistency of the dataset by evaluating the classification performance of each session using a baseline machine learning method. Conclusions The dataset includes the data of multiple recording sessions, various classes within the single upper extremity, and multimodal signals. This work can be used to (i) compare the brain activities associated with real movement and imagination, (ii) improve the decoding performance, and (iii) analyze the differences among recording sessions. Hence, this study, as a Data Note, has focused on collecting data required for further advances in the BCI technology.

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Section: Data Validationmentioning

confidence: 99%

Multimodal signal dataset for 11 intuitive movement tasks from single upper extremity during multiple recording sessions

et al. 2020

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“…Electrical source imaging (ESI) is one such approach that uses the electrical properties and geometry of the head to mitigate the effects of volume conduction and estimate cortical activity (26). Dramatic improvements in offline neural decoding have been observed when using ESI compared to traditional sensor techniques (24, 27); however, these approaches have yet to be validated online. By developing a real-time ESI platform, we were able to isolate and evaluate neural decoding in both the sensor and source domain without introducing the confounding online processing steps that often accompany other spatial filtering techniques (different classifiers, time windows, etc.…”

Section: Introductionmentioning

confidence: 99%

Noninvasive neuroimaging enhances continuous neural tracking for robotic device control

et al. 2019

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Brain-computer interfaces (BCIs) utilizing signals acquired with intracortical implants have achieved successful high-dimensional robotic device control useful for completing daily tasks. However, the substantial amount of medical and surgical expertise required to correctly implant and operate these systems significantly limits their use beyond a few clinical cases. A noninvasive counterpart requiring less intervention that can provide high-quality control would profoundly impact the integration of BCIs into the clinical and home setting. Here, we present and validate a noninvasive framework utilizing electroencephalography (EEG) to achieve the neural control of a robotic device for continuous random target tracking. This framework addresses and improves upon both the “brain” and “computer” components by respectively increasing user engagement through a continuous pursuit task and associated training paradigm, and the spatial resolution of noninvasive neural data through EEG source imaging. In all, our unique framework enhanced BCI learning by nearly 60% for traditional center-out tasks and by over 500% in the more realistic continuous pursuit task. We further demonstrated an additional enhancement in BCI control of almost 10% by using online noninvasive neuroimaging. Finally, this framework was deployed in a physical task, demonstrating a near seamless transition from the control of an unconstrained virtual cursor to the real-time control of a robotic arm. Such combined advances in the quality of neural decoding and the practical utility of noninvasive robotic arm control will have major implications on the eventual development and implementation of neurorobotics by means of noninvasive BCI.

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“…Due to the necessity of additional constraints and the fact that the EEG and its underlying sources are not fully understood yet, new SLMs are continuously developed (Pizzagalli, 2007 ; Grech et al, 2008 ; Becker et al, 2015 ). For BCIs, mainly the well-known, most established SLMs have been applied, like minimum norm (Noirhomme et al, 2008 ; Besserve et al, 2011 ; Edelman et al, 2014 ; Wronkiewicz et al, 2015 , 2016 ), weighted minimum norm (Qin et al, 2004 ; Babiloni et al, 2007 ; Kamousi et al, 2007 ; Cincotti et al, 2008 ; Yuan and He, 2009 ; Goel et al, 2011 ; Edelman et al, 2015 , 2016 ), standardized low resolution electromagnetic tomography (Congedo et al, 2006 ; Lotte et al, 2009 ; Handiru et al, 2017 ), local autoregressive average (Menendez et al, 2005 ; Poolman et al, 2008 ) and beamformer methods (Grosse-Wentrup et al, 2009 ; Ahn et al, 2012 ). However, a comparison of different distributed SLMs has rarely been reported so far.…”

Section: Introductionmentioning

confidence: 99%

Empirical Comparison of Distributed Source Localization Methods for Single-Trial Detection of Movement Preparation

Seeland

Krell

Straube

et al. 2018

Front. Hum. Neurosci.

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The development of technologies for the treatment of movement disorders, like stroke, is still of particular interest in brain-computer interface (BCI) research. In this context, source localization methods (SLMs), that reconstruct the cerebral origin of brain activity measured outside the head, e.g., via electroencephalography (EEG), can add a valuable insight into the current state and progress of the treatment. However, in BCIs SLMs were often solely considered as advanced signal processing methods that are compared against other methods based on the classification performance alone. Though, this approach does not guarantee physiological meaningful results. We present an empirical comparison of three established distributed SLMs with the aim to use one for single-trial movement prediction. The SLMs wMNE, sLORETA, and dSPM were applied on data acquired from eight subjects performing voluntary arm movements. Besides the classification performance as quality measure, a distance metric was used to asses the physiological plausibility of the methods. For the distance metric, which is usually measured to the source position of maximum activity, we further propose a variant based on clusters that is better suited for the single-trial case in which several sources are likely and the actual maximum is unknown. The two metrics showed different results. The classification performance revealed no significant differences across subjects, indicating that all three methods are equally well-suited for single-trial movement prediction. On the other hand, we obtained significant differences in the distance measure, favoring wMNE even after correcting the distance with the number of reconstructed clusters. Further, distance results were inconsistent with the traditional method using the maximum, indicating that for wMNE the point of maximum source activity often did not coincide with the nearest activation cluster. In summary, the presented comparison might help users to select an appropriate SLM and to understand the implications of the selection. The proposed methodology pays attention to the particular properties of distributed SLMs and can serve as a framework for further comparisons.

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EEG source space analysis of the supervised factor analytic approach for the classification of multi-directional arm movement

Abstract: This study presents evidence that low-frequency components in the source space play an important role in movement kinematics, and thus it may lead to new strategies for BCI-based neurorehabilitation.

Cited by 24 publications

References 62 publications

Multimodal signal dataset for 11 intuitive movement tasks from single upper extremity during multiple recording sessions

Multimodal signal dataset for 11 intuitive movement tasks from single upper extremity during multiple recording sessions

Noninvasive neuroimaging enhances continuous neural tracking for robotic device control

Empirical Comparison of Distributed Source Localization Methods for Single-Trial Detection of Movement Preparation

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