A brain-computer interface with vibrotactile biofeedback for haptic information

Chatterjee, Aniruddha; Aggarwal, Vikram; Ramos, Ander; Acharya, Soumyadipta; Thakor, Nitish V.

doi:10.1186/1743-0003-4-40

Cited by 172 publications

(112 citation statements)

References 32 publications

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“…In practice, physiologically meaningful EEG features can be extracted from several frequency bands of recorded EEG signals. Therefore, many electrical brain activities have been used in EEG-based BCI systems, e.g., μ rhythm [3][4][5][6][7], slow cortical potential [8], event-related P300 [9,10] and steady-state visual evoked potential [11,12]. The activity most widely used to monitor the brain for BCI applications is the μ rhythm, which is related to motor actions [2,3,13,14].…”

Section: Introductionmentioning

confidence: 99%

Executed Movement Using EEG Signals through a Naive Bayes Classifier

Machado

Balbinot

2014

Micromachines

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Recent years have witnessed a rapid development of brain-computer interface (BCI) technology. An independent BCI is a communication system for controlling a device by human intension, e.g., a computer, a wheelchair or a neuroprosthes is, not depending on the brain's normal output pathways of peripheral nerves and muscles, but on detectable signals that represent responsive or intentional brain activities. This paper presents a comparative study of the usage of the linear discriminant analysis (LDA) and the naive Bayes (NB) classifiers on describing both right-and left-hand movement through electroencephalographic signal (EEG) acquisition. For the analysis, we considered the following input features: the energy of the segments of a band pass-filtered signal with the frequency band in sensorimotor rhythms and the components of the spectral energy obtained through the Welch method. We also used the common spatial pattern (CSP) filter, so as to increase the discriminatory activity among movement classes. By using the database generated by this experiment, we obtained hit rates up to 70%. The results are compatible with previous studies.

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Section: Introductionmentioning

confidence: 99%

Executed Movement Using EEG Signals through a Naive Bayes Classifier

Machado

Balbinot

2014

Micromachines

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“…Steady-state visual evoked potentials have been used for binary selection, [48][49][50][51] both discrete and continuous control of a cursor in 2 dimensions, 52 prosthesis control, 53 FES, 54,55 spelling, 56 and environmental control. 57 For patients with impaired vision, various auditory 39,40,[58][59][60][61] and tactile [62][63][64][65] paradigms have been investigated. A few studies are now focused on the critical need to move BCI systems out of the laboratory and into patients' homes, which raises many complex patient, caregiver, and implementation issues.…”

Section: Device Outputmentioning

confidence: 99%

Brain-Computer Interfaces in Medicine

Shih

Krusienski

Wolpaw

2012

Mayo Clinic Proceedings

565

279

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Brain-computer interfaces (BCIs) acquire brain signals, analyze them, and translate them into commands that are relayed to output devices that carry out desired actions. BCIs do not use normal neuromuscular output pathways. The main goal of BCI is to replace or restore useful function to people disabled by neuromuscular disorders such as amyotrophic lateral sclerosis, cerebral palsy, stroke, or spinal cord injury. From initial demonstrations of electroencephalography-based spelling and single-neuron-based device control, researchers have gone on to use electroencephalographic, intracortical, electrocorticographic, and other brain signals for increasingly complex control of cursors, robotic arms, prostheses, wheelchairs, and other devices. Brain-computer interfaces may also prove useful for rehabilitation after stroke and for other disorders. In the future, they might augment the performance of surgeons or other medical professionals. Brain-computer interface technology is the focus of a rapidly growing research and development enterprise that is greatly exciting scientists, engineers, clinicians, and the public in general. Its future achievements will depend on advances in 3 crucial areas. Brain-computer interfaces need signal-acquisition hardware that is convenient, portable, safe, and able to function in all environments. Brain-computer interface systems need to be validated in long-term studies of real-world use by people with severe disabilities, and effective and viable models for their widespread dissemination must be implemented. Finally, the day-to-day and moment-to-moment reliability of BCI performance must be improved so that it approaches the reliability of natural muscle-based function. U ntil recently, the dream of being able to control one's environment through thoughts had been in the realm of science fiction. However, the advance of technology has brought a new reality: Today, humans can use the electrical signals from brain activity to interact with, influence, or change their environments. The emerging field of brain-computer interface (BCI) technology may allow individuals unable to speak and/or use their limbs to once again communicate or operate assistive devices for walking and manipulating objects. Brain-computer interface research is an area of high public awareness. Videos on YouTube as well as news reports in the lay media indicate intense curiosity and interest in a field that hopefully one day soon will dramatically improve the lives of many disabled persons affected by a number of different disease processes.This review seeks to provide the general medical community with an introduction to BCIs. We define BCI and then review some of the seminal discoveries in this rapidly emerging field, the brain signals used by BCIs, the essential components of a BCI system, current BCI systems, and the key issues now engaging researchers. Challenges are inherent in translating any new technology to practical and useful clinical applications, and BCIs are no exception. We discuss the potent...

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“…Several recent studies described the addition of vibrotactile sensory channels to EEG-based BMIs (Chatterjee et al, 2007;Cincotti et al, 2007). While sensory substitution using haptic devices (Vidal-Verdú & Hafez, 2007) is clearly an important development that will benefit patients, sensorizing BMIs with ICMS is potentially capable of surpassing sensory substitution.…”

Section: Sensory Substitutionmentioning

confidence: 99%

Brain-Machine-Brain Interface

O’Doherty¹

2015

Encyclopedia of Computational Neuroscience

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I first show that a direct intracortical signal can be used to instruct rhesus monkeys about the direction of a reach to make with a BMI. Rhesus monkeys placed an actuator over an instruction target and obtained, from the target's artificial texture, information about the correct reach path. Initially these somatosensory instructions took the form of vibrotactile stimulation of the hands. Next, ICMS of primary somatosensory cortex (S1) iv in one monkey and posterior parietal cortex (PPC) in another was substituted for this peripheral somatosensory signal. Finally, the monkeys made direct brain-controlled reaches using the activity of ensembles of primary motor cortex (M1) cells, conditional on the ICMS cues. The monkey receiving ICMS of S1 was able to achieve the same level of proficiency with ICMS as with the stimulus delivered to the skin of the hand. The monkey receiving ICMS of PPC was unable to perform the task above chance. This experiment indicates that ICMS of S1 can form the basis of an afferent prosthetic input to the brain for guiding brain-controlled prostheses.I next show that ICMS of S1 can provide feedback about the interactions of a virtualreality upper-limb avatar and virtual objects, enabling active touch. Rhesus monkeys initially controlled the avatar with the movements of their arms and used it to search through sets of up to three objects. Feedback in the form of temporal patterns of ICMS occurred whenever the avatar touched a virtual object. Monkeys learned to use this feedback to find the objects with particular artificial textures, as encoded by the ICMS patterns, and select those associated with reward while avoiding selecting the non-rewarded objects. Next, the control of the avatar was switched to direct brain-control and the monkeys continued to move the avatar with motor commands derived from the extracellular neuronal activity of M1 cells. The afferent and efferent modules of this BMBI were temporally interleaved, and as such did not interfere with each other, yet allowed effectively concurrent operation. Cortical motor neurons were measured while the monkey passively observed the movements of the avatar and were found to be modulated, a result that suggests that concurrent visual and artificial somatosensory feedback lead to the incorporation of the avatar into the monkey's internal brain representation. In summary, rhesus monkeys were augmented with a bidirectional neural interface that allowed them to make reaches to objects and discriminate them by their textures-all without making actual movements and without relying on somatic sensation from their real bodies. Both action and perception were mediated by the brain-machine-brain interface.I probed the sensitivity of the afferent leg of the interface to precise temporal patterns of ICMS. Moreover, I describe evidence that the BMBI controlled avatar was incorporated into the monkey's internal brain representation. These results suggest that future clinical neuroprostheses could implement realistic feedback about object-actuator in...

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A brain-computer interface with vibrotactile biofeedback for haptic information

Cited by 172 publications

References 32 publications

Executed Movement Using EEG Signals through a Naive Bayes Classifier

Executed Movement Using EEG Signals through a Naive Bayes Classifier

Brain-Computer Interfaces in Medicine

Brain-Machine-Brain Interface

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