The thought translation device: a neurophysiological approach to communication in total motor paralysis

Kübler, Andrea; Kotchoubey, Boris; Hinterberger, Thilo; Ghanayim, Nimr; Perelmouter, J.; Schauer, Margarete; Fritsch, Christoph; Taub, Edward; Birbaumer, Niels

doi:10.1007/s002210050617

Cited by 250 publications

(154 citation statements)

References 19 publications

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“…figure 3). Using signal analysis and cursor control methods that were developed in BCI studies employing scalp-recorded EEG activity [33][34][35][36], this study found that ECoG-based control develops more quickly than EEG-based control [37,38], and is likely to be substantially more effective in providing communication and control to people with severe motor disabilities. Offline analysis of ECoG data recorded during a twodimensional (2D) center-out joystick task showed substantial directional information (figure 4).…”

Section: Discussionmentioning

confidence: 99%

A brain–computer interface using electrocorticographic signals in humans

et al. 2004

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Brain-computer interfaces (BCIs) enable users to control devices with electroencephalographic (EEG) activity from the scalp or with single-neuron activity from within the brain. Both methods have disadvantages: EEG has limited resolution and requires extensive training, while single-neuron recording entails significant clinical risks and has limited stability. We demonstrate here for the first time that electrocorticographic (ECoG) activity recorded from the surface of the brain can enable users to control a one-dimensional computer cursor rapidly and accurately. We first identified ECoG signals that were associated with different types of motor and speech imagery. Over brief training periods of 3-24 min, four patients then used these signals to master closed-loop control and to achieve success rates of 74-100% in a one-dimensional binary task. In additional open-loop experiments, we found that ECoG signals at frequencies up to 180 Hz encoded substantial information about the direction of two-dimensional joystick movements. Our results suggest that an ECoG-based BCI could provide for people with severe motor disabilities a non-muscular communication and control option that is more powerful than EEG-based BCIs and is potentially more stable and less traumatic than BCIs that use electrodes penetrating the brain. M This article features online multimedia enhancements

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Section: Discussionmentioning

confidence: 99%

A brain–computer interface using electrocorticographic signals in humans

et al. 2004

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“…Further, this system has been tested in people with late-stage ALS and has proved capable of providing basic communication capacities [17]. Often, these BCIs are based on visual feedback from a computer screen that shows one choice at the top and one at the bottom.…”

Section: Scp-based Bcismentioning

confidence: 99%

Brain–computer interfacing in disorders of consciousness

et al. 2012

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Background: Recent neuroimaging research has strikingly demonstrated the existence of covert awareness in some patients with disorders of consciousness (DoC). These findings have highlighted the potential for the development of simple brain-computer interfaces (BCI) for the significant minority of behaviourally unresponsive patients who display consistent signs of covert awareness.Objectives: In this article, we review current EEG-based BCIs that hold potential for assessing and eventually assisting patients with DoC. We highlight key areas for further development that might eventually make their application feasible in this challenging patient group.Methods: We consider the major types of BCIs proposed in the literature, namely those based on the P3 potential, sensorimotor rhythms, steady state oscillations, and slow cortical potentials. In each case, we provide a brief overview of the relevant literature, and then consider their relative merits for BCI applications in DoC.Results: Over the last few decades, a range of BCI designs have been proposed and tested for enabling communication in fully conscious, paralysed patients. Though many of these have potential applicability for patients with DoC, they share some key challenges that need to be overcome, including limitations of stimulation modality, feedback, user training and consistency. Conclusion: Developing feasible BCIs for diagnostics and communication in DoCwill require parallel strands of enquiry. Preliminary exploratory research will need to specifically tailor cognitive tasks that can tap into the forms in which patients could potentially express volition. Alongside, future work will need to address the technical and practical challenges facing reliable implementation at the patient's bedside.

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“…Moreover, the more automated a task is, the less is the involvement of high-level control centres, the smaller the amount of involved synapses and relays, the faster the task execution. However, BMI control efficiency reduction due to fatigue has been reported also in automatic control systems (Kennedy et al, 2000): this is, sometimes, caused by user attempt to speed up the control (Kübler et al, 1999).…”

Section: Discussionmentioning

confidence: 99%

Defining brain–machine interface applications by matching interface performance with device requirements

Tonet

Marinelli

Citi

et al. 2008

Journal of Neuroscience Methods

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Interaction with machines is mediated by Human-Machine Interfaces (HMIs). Brainmachine interfaces (BMIs) are a particular class of HMIs and have so far been studied as a communication means for people who have little or no voluntary control of muscle activity. In this context, low-performing interfaces can be considered as prosthetic applications. On the other hand, for able-bodied users, a BMI would only be practical if conceived as an augmenting interface. In this paper a method is introduced for pointing out effective combinations of interfaces and devices for creating real-world applications. First, devices for domotics, rehabilitation and assistive robotics, and their requirements, in terms of throughput and latency, are described. Second, HMIs are classified and their performance described, still in terms of throughput and latency. Then device requirements are matched with performance of available interfaces. Simple rehabilitation and domotics devices can be easily controlled by means of BMI technology. Prosthetic hands and wheelchairs are suitable applications but do not attain optimal interactivity. Regarding humanoid robotics, the head and the trunk can be controlled by means of BMIs, while other parts require too much throughput. Robotic arms, which have been controlled by means of cortical invasive interfaces in animal studies, could be the next frontier for non-invasive BMIs. Combining smart controllers with BMIs could improve interactivity and boost BMI applications.

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The thought translation device: a neurophysiological approach to communication in total motor paralysis

Cited by 250 publications

References 19 publications

A brain–computer interface using electrocorticographic signals in humans

A brain–computer interface using electrocorticographic signals in humans

Brain–computer interfacing in disorders of consciousness

Defining brain–machine interface applications by matching interface performance with device requirements

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