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      Characteristics of Kinematic Parameters in Decoding Intended Reaching Movements Using Electroencephalography (EEG)

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          Abstract

          The utility of premovement electroencephalography (EEG) for decoding movement intention during a reaching task has been demonstrated. However, the kind of information the brain represents regarding the intended target during movement preparation remains unknown. In the present study, we investigated which movement parameters (i.e., direction, distance, and positions for reaching) can be decoded in premovement EEG decoding. Eight participants performed 30 types of reaching movements that consisted of 1 of 24 movement directions, 7 movement distances, 5 horizontal target positions, and 5 vertical target positions. Event-related spectral perturbations were extracted using independent components, some of which were selected via an analysis of variance for further binary classification analysis using a support vector machine. When each parameter was used for class labeling, all possible binary classifications were performed. Classification accuracies for direction and distance were significantly higher than chance level, although no significant differences were observed for position. For the classification in which each movement was considered as a different class, the parameters comprising two vectors representing each movement were analyzed. In this case, classification accuracies were high when differences in distance were high, the sum of distances was high, angular differences were large, and differences in the target positions were high. The findings further revealed that direction and distance may provide the largest contributions to movement. In addition, regardless of the parameter, useful features for classification are easily found over the parietal and occipital areas.

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          Most cited references43

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          What is the Bereitschaftspotential?

          Since discovery of the slow negative electroencephalographic (EEG) activity preceding self-initiated movement by Kornhuber and Deecke [Kornhuber HH, Deecke L. Hirnpotentialänderungen bei Willkurbewegungen und passiven Bewegungen des Menschen: Bereitschaftspotential und reafferente Potentiale. Pflugers Archiv 1965;284:1-17], various source localization techniques in normal subjects and epicortical recording in epilepsy patients have disclosed the generator mechanisms of each identifiable component of movement-related cortical potentials (MRCPs) to some extent. The initial slow segment of BP, called 'early BP' in this article, begins about 2 s before the movement onset in the pre-supplementary motor area (pre-SMA) with no site-specificity and in the SMA proper according to the somatotopic organization, and shortly thereafter in the lateral premotor cortex bilaterally with relatively clear somatotopy. About 400 ms before the movement onset, the steeper negative slope, called 'late BP' in this article (also referred to as NS'), occurs in the contralateral primary motor cortex (M1) and lateral premotor cortex with precise somatotopy. These two phases of BP are differentially influenced by various factors, especially by complexity of the movement which enhances only the late BP. Event-related desynchronization (ERD) of beta frequency EEG band before self-initiated movements shows a different temporospatial pattern from that of the BP, suggesting different neuronal mechanisms for the two. BP has been applied for investigating pathophysiology of various movement disorders. Volitional motor inhibition or muscle relaxation is preceded by BP quite similar to that preceding voluntary muscle contraction. Since BP of typical waveforms and temporospatial pattern does not occur before organic involuntary movements, BP is used for detecting the participation of the 'voluntary motor system' in the generation of apparently involuntary movements in patients with psychogenic movement disorders. In view of Libet et al.'s report [Libet B, Gleason CA, Wright EW, Pearl DK. Time of conscious intention to act in relation to onset of cerebral activity (readiness-potential). The unconscious initiation of a freely voluntary act. Brain 1983;106:623-642] that the awareness of intention to move occurred much later than the onset of BP, the early BP might reflect, physiologically, slowly increasing cortical excitability and, behaviorally, subconscious readiness for the forthcoming movement. Whether the late BP reflects conscious preparation for intended movement or not remains to be clarified.
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            Intention, action planning, and decision making in parietal-frontal circuits.

            The posterior parietal cortex and frontal cortical areas to which it connects are responsible for sensorimotor transformations. This review covers new research on four components of this transformation process: planning, decision making, forward state estimation, and relative-coordinate representations. These sensorimotor functions can be harnessed for neural prosthetic operations by decoding intended goals (planning) and trajectories (forward state estimation) of movements as well as higher cortical functions related to decision making and potentially the coordination of multiple body parts (relative-coordinate representations).
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              Complex movements evoked by microstimulation of precentral cortex.

              Electrical microstimulation was used to study primary motor and premotor cortex in monkeys. Each stimulation train was 500 ms in duration, approximating the time scale of normal reaching and grasping movements and the time scale of the neuronal activity that normally accompanies movement. This stimulation on a behaviorally relevant time scale evoked coordinated, complex postures that involved many joints. For example, stimulation of one site caused the mouth to open and also caused the hand to shape into a grip posture and move to the mouth. Stimulation of this site always drove the joints toward this final posture, regardless of the direction of movement required to reach the posture. Stimulation of other cortical sites evoked different postures. Postures that involved the arm were arranged across cortex to form a map of hand positions around the body. This stimulation-evoked map encompassed both primary motor and the adjacent premotor cortex. We suggest that these regions fit together into a single map of the workspace around the body.
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                Author and article information

                Contributors
                Journal
                Front Neurosci
                Front Neurosci
                Front. Neurosci.
                Frontiers in Neuroscience
                Frontiers Media S.A.
                1662-4548
                1662-453X
                01 November 2019
                2019
                : 13
                : 1148
                Affiliations
                [1] 1Department of Information and Communications Engineering, Tokyo Institute of Technology , Yokohama, Japan
                [2] 2Institute of Innovative Research, Tokyo Institute of Technology , Yokohama, Japan
                [3] 3Precursory Research for Embryonic Science and Technology (PRESTO), Japan Science and Technology Agency (JST) , Saitama, Japan
                Author notes

                Edited by: Waldemar Karwowski, University of Central Florida, United States

                Reviewed by: Emiliano Brunamonti, Sapienza University of Rome, Italy; Ben D. Sawyer, University of Central Florida, United States

                *Correspondence: Yasuharu Koike, koike@ 123456pi.titech.ac.jp

                This article was submitted to Neural Technology, a section of the journal Frontiers in Neuroscience

                Article
                10.3389/fnins.2019.01148
                6838638
                31736690
                a6d8a5a5-2244-43b2-bd8d-6c83f8f1367f
                Copyright © 2019 Kim, Yoshimura and Koike.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 27 June 2019
                : 11 October 2019
                Page count
                Figures: 11, Tables: 1, Equations: 0, References: 51, Pages: 13, Words: 0
                Categories
                Neuroscience
                Original Research

                Neurosciences
                brain–machine interface (bmi),electroencephalography (eeg),classification,premovement,decoding

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