A high performance sensorimotor beta rhythm-based brain–computer interface associated with human natural motor behavior

Bai, Ou; Lin, Peter; Vorbach, Sherry; Floeter, Mary Kay; Hattori, Noriaki; Hallett, Mark

doi:10.1088/1741-2560/5/1/003

Cited by 133 publications

(105 citation statements)

References 50 publications

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“…Classically, ␤-oscillatory regulation has been reported within sensory-motor tasks like motor imagery (Bai et al, 2008;Waldert et al, 2008), voluntary movement control (Pogosyan et al, 2009), and anticipatory perceptual decision making (Donner et al, 2009). But regulating ␤-oscillations has, in particular, been implicated in top-down control in perceptual and cognitive operations (for review, see Engel and Fries, 2010), including predictive coding of upcoming perceptual events (Roelfsema et al, 1997;Bastos et al, 2012), visual search (Buschman andMiller, 2007), perceptual change in bistable images (Okazaki et al, 2008), and ambiguous auditory sounds (Iversen et al, 2009).…”

Section: Prestimulus Oscillatory Powermentioning

confidence: 99%

Temporal Windows in Visual Processing: “Prestimulus Brain State” and “Poststimulus Phase Reset” Segregate Visual Transients on Different Temporal Scales

et al. 2014

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Dynamic vision requires both stability of the current perceptual representation and sensitivity to the accumulation of sensory evidence over time. Here we study the electrophysiological signatures of this intricate balance between temporal segregation and integration in vision. Within a forward masking paradigm with short and long stimulus onset asynchronies (SOA), we manipulated the temporal overlap of the visual persistence of two successive transients. Human observers enumerated the items presented in the second target display as a measure of the informational capacity read-out from this partly temporally integrated visual percept. We observed higher ␤-power immediately before mask display onset in incorrect trials, in which enumeration failed due to stronger integration of mask and target visual information. This effect was timescale specific, distinguishing between segregation and integration of visual transients that were distant in time (long SOA). Conversely, for short SOA trials, mask onset evoked a stronger visual response when mask and targets were correctly segregated in time. Examination of the target-related response profile revealed the importance of an evoked ␣-phase reset for the segregation of those rapid visual transients. Investigating this precise mapping of the temporal relationships of visual signals onto electrophysiological responses highlights how the stream of visual information is carved up into discrete temporal windows that mediate between segregated and integrated percepts. Fragmenting the stream of visual information provides a means to stabilize perceptual events within one instant in time.

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Section: Prestimulus Oscillatory Powermentioning

confidence: 99%

Temporal Windows in Visual Processing: “Prestimulus Brain State” and “Poststimulus Phase Reset” Segregate Visual Transients on Different Temporal Scales

et al. 2014

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“…The exogenous system uses the signals such as visual evoked potentials (VEP), steady state visual evoked potentials and P300 [1]. These BCI paradigms have successfully conveying the brain signals into the control commands [6]. Compared to other BCI paradigms, SSVEP based BCI has the advantages of high accuracy rate, speed, scalability and less training time [2][7] [8].…”

Section: Introductionmentioning

confidence: 99%

The Effect of the Viewing Distance of Stimulus on SSVEP Response for Use in Brain-Computer Interfaces

Lakany

2013

2013 IEEE International Conference on Systems, Man, and Cybernetics

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Abstract-Most visual stimulators used in steady state visual evoked potential (SSVEP) based brain-computer interface (BCI) to elicit SSVEP response are set close to the BCI system limiting the portability and the applications of the system. Our aim is to develop a portable SSVEP based BCI adaptable to the change of the viewing distance of the stimulator which will allow users to operate the BCI without being restricted to a particular area. This study (1) investigates the impact of the distance between the user and the visual stimulator on the SSVEP response and (2) develops a portable visual stimulator that would adapt to change of distance. The finding suggests that a distance adaptable SSVEP based BCI is achievable by changing the intensity of visual stimulator to correspond to the change of the viewing distance. It is hoped that this work can help SSVEP BCI design and broaden its potential applications and users.Keywords: Brain-computer interface (BCI), Steady state visual evoked potential (SSVEP), Electroencephalography (EEG)

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“…Besides EEG-and electrocorticograph-based systems in humans (Leuthardt et al, 2004;Wolpaw and McFarland, 2004;Bai et al, 2008), invasive NIs in nonhuman primates have been developed using neural population activity in primary motor cortex (M1) to reconstruct continuous 2D and 3D arm and hand position (Wessberg et al, 2000;Serruya et al, 2002;Taylor et al, 2002;Carmena et al, 2003), and monkeys have learned to use these signals to control a gripper-equipped robotic arm to feed themselves (Velliste et al, 2008). This approach has generally not yet been extended to decode sophisticated grasping patterns, which is attributable to the complex nature of dexterous finger movements and the large number of degrees of freedom of the hand (Schieber and Santello, 2004); however, see Vargas-Irwin et al (2010) and Carpaneto et al (2011) for first examples of such an approach.…”

Section: Introductionmentioning

confidence: 99%

Grasp Movement Decoding from Premotor and Parietal Cortex

Townsend¹,

Subasi²,

Scherberger³

2011

J. Neurosci.

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Despite recent advances in harnessing cortical motor-related activity to control computer cursors and robotic devices, the ability to decode and execute different grasping patterns remains a major obstacle. Here we demonstrate a simple Bayesian decoder for real-time classification of grip type and wrist orientation in macaque monkeys that uses higher-order planning signals from anterior intraparietal cortex (AIP) and ventral premotor cortex (area F5). Real-time decoding was based on multiunit signals, which had similar tuning properties to cells in previous single-unit recording studies. Maximum decoding accuracy for two grasp types (power and precision grip) and five wrist orientations was 63% (chance level, 10%). Analysis of decoder performance showed that grip type decoding was highly accurate (90.6%), with most errors occurring during orientation classification. In a subsequent off-line analysis, we found small but significant performance improvements (mean, 6.25 percentage points) when using an optimized spike-sorting method (superparamagnetic clustering). Furthermore, we observed significant differences in the contributions of F5 and AIP for grasp decoding, with F5 being better suited for classification of the grip type and AIP contributing more toward decoding of object orientation. However, optimum decoding performance was maximal when using neural activity simultaneously from both areas. Overall, these results highlight quantitative differences in the functional representation of grasp movements in AIP and F5 and represent a first step toward using these signals for developing functional neural interfaces for hand grasping.

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A high performance sensorimotor beta rhythm-based brain–computer interface associated with human natural motor behavior

Abstract: The proposed new non-invasive BCI method highlights a practical BCI for clinical applications, where the user does not require extensive training.

Cited by 133 publications

References 50 publications

Temporal Windows in Visual Processing: “Prestimulus Brain State” and “Poststimulus Phase Reset” Segregate Visual Transients on Different Temporal Scales

Temporal Windows in Visual Processing: “Prestimulus Brain State” and “Poststimulus Phase Reset” Segregate Visual Transients on Different Temporal Scales

The Effect of the Viewing Distance of Stimulus on SSVEP Response for Use in Brain-Computer Interfaces

Grasp Movement Decoding from Premotor and Parietal Cortex

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