Detection of the onset of upper-limb movements based on the combined analysis of changes in the sensorimotor rhythms and slow cortical potentials

Ibáñez, Jaime; Serrano, J. Ignacio; Castillo, M. Dolores del; Monge-Pereira, Esther; Molina‐Rueda, Francisco; Alguacil-Diego, Isabel M.; Pons, José L

doi:10.1088/1741-2560/11/5/056009

Cited by 83 publications

(98 citation statements)

References 46 publications

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“…The majority of BCI studies were focused on techniques that do not respond to wishes and intents directly, such as motor imagery and responses to stimuli. Recently, slow premotor potentials were employed for developing neurorehabilitation BCIs (Niazi et al, 2011; Ibáñez et al, 2014; Lew et al, 2014; Xu et al, 2014; Jiang et al, 2015; Shakeel et al, 2015). But probably only the works by Zander's group (Ihme and Zander, 2011; Protzak et al, 2013) addressed the direct conversion of intentions into actions in line with the elegant approach by Gray Walter, now also enhanced with gaze capabilities.…”

Section: Discussionmentioning

confidence: 99%

EEG Negativity in Fixations Used for Gaze-Based Control: Toward Converting Intentions into Actions with an Eye-Brain-Computer Interface

et al. 2016

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We usually look at an object when we are going to manipulate it. Thus, eye tracking can be used to communicate intended actions. An effective human-machine interface, however, should be able to differentiate intentional and spontaneous eye movements. We report an electroencephalogram (EEG) marker that differentiates gaze fixations used for control from spontaneous fixations involved in visual exploration. Eight healthy participants played a game with their eye movements only. Their gaze-synchronized EEG data (fixation-related potentials, FRPs) were collected during game's control-on and control-off conditions. A slow negative wave with a maximum in the parietooccipital region was present in each participant's averaged FRPs in the control-on conditions and was absent or had much lower amplitude in the control-off condition. This wave was similar but not identical to stimulus-preceding negativity, a slow negative wave that can be observed during feedback expectation. Classification of intentional vs. spontaneous fixations was based on amplitude features from 13 EEG channels using 300 ms length segments free from electrooculogram contamination (200–500 ms relative to the fixation onset). For the first fixations in the fixation triplets required to make moves in the game, classified against control-off data, a committee of greedy classifiers provided 0.90 ± 0.07 specificity and 0.38 ± 0.14 sensitivity. Similar (slightly lower) results were obtained for the shrinkage Linear Discriminate Analysis (LDA) classifier. The second and third fixations in the triplets were classified at lower rate. We expect that, with improved feature sets and classifiers, a hybrid dwell-based Eye-Brain-Computer Interface (EBCI) can be built using the FRP difference between the intended and spontaneous fixations. If this direction of BCI development will be successful, such a multimodal interface may improve the fluency of interaction and can possibly become the basis for a new input device for paralyzed and healthy users, the EBCI “Wish Mouse.”

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Section: Discussionmentioning

confidence: 99%

EEG Negativity in Fixations Used for Gaze-Based Control: Toward Converting Intentions into Actions with an Eye-Brain-Computer Interface

et al. 2016

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“…To study statistical differences in the frequency distribution of the selected features, they were divided into three groups: alpha band (7-12 Hz), lower-beta band (13)(14)(15)(16)(17)(18)(19), and upper-beta band (20)(21)(22)(23)(24)(25)(26)(27)(28)(29). Normalization was also performed in this case.…”

Section: Statistical Analysis Of the Features Selectedmentioning

confidence: 99%

“…A number of studies with EEG have demonstrated its potential use to locate intervals of motorrelated cortical activation and deactivation [24,33], to anticipate the instants at which voluntary movements begin [4,18,19,22,37], to decode movement parameters such as velocity and strength [15], and to distinguish between different classes of movements [23,30]. Yet, it remains unclear the extent to which the EEG activity allows the description of motor-related mental processes.…”

Section: Introductionmentioning

confidence: 99%

Predictive classification of self-paced upper-limb analytical movements with EEG

Ibáñez

Serrano

Castillo

et al. 2015

Med Biol Eng Comput

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The extent to which the electroencephalographic activity allows the characterization of movements with the upper limb is an open question. This paper describes the design and validation of a classifier of upper-limb analytical movements based on electroencephalographic activity extracted from intervals preceding self-initiated movement tasks. Features selected for the classification are subject specific and associated with the movement tasks. Further tests are performed to reject the hypothesis that other information different from the task-related cortical activity is being used by the classifiers. Six healthy subjects were measured performing self-initiated upper-limb analytical movements. A Bayesian classifier was used to classify among seven different kinds of movements. Features considered covered the alpha and beta bands. A genetic algorithm was used to optimally select a subset of features for the classification. An average accuracy of 62.9 ± 7.5% was reached, which was above the baseline level observed with the proposed methodology (30.2 ± 4.3%). The study shows how the electroencephalography carries information about the type of analytical movement performed with the upper limb and how it can be decoded before the movement begins. In neurorehabilitation environments, this information could be used for monitoring and assisting purposes.

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“…However, detection of movement onset from EEG can be highly variable, ranging at least 100–300 ms [57,58]. Reliability can be improved with longer sampling times, but at the expense of even greater latency [59].…”

Section: Guiding Plasticity For Neurorehabilitationmentioning

confidence: 99%

Brain-controlled neuromuscular stimulation to drive neural plasticity and functional recovery

Éthier¹,

Gallego²,

Miller³

2015

Current Opinion in Neurobiology

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There is mounting evidence that appropriately timed neuromuscular stimulation can induce neural plasticity and generate functional recovery from motor disorders. This review addresses the idea that coordinating stimulation with a patient’s voluntary effort might further enhance neurorehabilitation. Studies in cell cultures and behaving animals have delineated the rules underlying neural plasticity when single neurons are used as triggers. However, the rules governing more complex stimuli and larger networks are less well understood. We argue that functional recovery might be optimized if stimulation were modulated by a brain machine interface, to matched the details of the patient’s voluntary intent. The potential of this novel approach highlights the need for a better understanding of the complex rules underlying this form of plasticity.

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Detection of the onset of upper-limb movements based on the combined analysis of changes in the sensorimotor rhythms and slow cortical potentials

Cited by 83 publications

References 46 publications

EEG Negativity in Fixations Used for Gaze-Based Control: Toward Converting Intentions into Actions with an Eye-Brain-Computer Interface

EEG Negativity in Fixations Used for Gaze-Based Control: Toward Converting Intentions into Actions with an Eye-Brain-Computer Interface

Predictive classification of self-paced upper-limb analytical movements with EEG

Brain-controlled neuromuscular stimulation to drive neural plasticity and functional recovery

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