A P300-based brain–computer interface for people with amyotrophic lateral sclerosis

Nijboer, Femke; Sellers, Eric W.; Mellinger, Jürgen; Jordan, Mathias; Matuz, Tamara; Furdea, Adrian; Halder, Sebastian; Mochty, Ursula; Krusienski, Dean J.; Vaughan, Theresa M.; Wolpaw, Jonathan R.; Birbaumer, Niels; Kübler, Andrea

doi:10.1016/j.clinph.2008.03.034

Cited by 553 publications

(469 citation statements)

References 20 publications

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“…BCI systems can reliably evoke and measure event-related potentials (ERP) such as the P300, and use them to spell words and move computer cursors based on a user's intent [8][9][10]13]. This proves very valuable in restoring the ability to communicate for patients suffering from the locked-in syndrome and other neurological diseases, and can be generalized to healthy users as well.…”

Section: Eeg and Hcimentioning

confidence: 99%

I Think, Therefore I Am: Usability and Security of Authentication Using Brainwaves

Chuang

Nguyen

Wang

et al. 2013

Lecture Notes in Computer Science

130

115

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Abstract. With the embedding of EEG (electro-encephalography) sensors in wireless headsets and other consumer electronics, authenticating users based on their brainwave signals has become a realistic possibility. We undertake an experimental study of the usability and performance of user authentication using consumer-grade EEG sensor technology. By choosing custom tasks and custom acceptance thresholds for each subject, we can achieve 99% authentication accuracy using single-channel EEG signals, which is on par with previous research employing multichannel EEG signals using clinical-grade devices. In addition to the usability improvement offered by the single-channel dry-contact EEG sensor, we also study the usability of different classes of mental tasks. We find that subjects have little difficulty recalling chosen "pass-thoughts" (e.g., their previously selected song to sing in their mind). They also have different preferences for tasks based on the perceived difficulty and enjoyability of the tasks. These results can inform the design of authentication systems that guide users in choosing tasks that are both usable and secure.

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Section: Eeg and Hcimentioning

confidence: 99%

I Think, Therefore I Am: Usability and Security of Authentication Using Brainwaves

Chuang

Nguyen

Wang

et al. 2013

Lecture Notes in Computer Science

130

115

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“…The device operation provides feedback to the user, thus closing the control loop. 19 Because of its relative ease of implementation and performance, one of the most researched BCI paradigms is the visual P300 speller, 5 which has been demonstrated successfully in both healthy and disabled persons for typing, [35][36][37][38][39][40][41][42] Internet browsing, 43 guidance of a wheelchair along predetermined paths, [44][45][46][47] and other applications. Like the P300 evoked response, steady-state visual evoked potentials are innate and require no training, but they are capable of providing faster response times.…”

Section: Device Outputmentioning

confidence: 99%

Brain-Computer Interfaces in Medicine

Shih

Krusienski

Wolpaw

2012

Mayo Clinic Proceedings

562

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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“…In the past, visual P300 responses have been widely investigated for implementing BCIs [e.g. 13,14], and in particular for creating speller applications [15,[19][20][21]. Similarly, auditory P300 responses have been used for implementing speller applications, e.g.…”

Section: Related Workmentioning

confidence: 99%

A low-cost auditory multi-class brain-computer interface based on pitch, spatial and timbre cues

Vamvakousis

Ramírez

2013

Preprint

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P300-based brain-computer interfaces (BCIs) are especially useful for people with illnesses, which prevent them from communicating in a normal way (e.g. brain or spinal cord injury).However, most of the existing P300-based BCI systems use visual stimulation which may not be suitable for patients with sight deterioration (e.g. patients suffering from amyotrophic lateral sclerosis). Moreover, P300-based BCI systems rely on expensive equipment, which greatly limits their use outside the clinical environment. Therefore, we propose a multi-class BCI system based solely on auditory stimuli, which makes use of low-cost EEG technology.We explored different combinations of timbre, pitch and spatial auditory stimuli (TimPiSp:timbre-pitch-spatial, TimSp: timbre-spatial, and Timb: timbre-only) and three inter-stimulus intervals (150ms, 175ms and 300ms), and evaluated our system by conducting an oddball task on 7 healthy subjects. This is the first study in which these 3 auditory cues are compared. After averaging several repetitions in the 175ms inter-stimulus interval, we obtained average selection accuracies of 97.14%, 91.43%, and 88.57% for modalities TimPiSp, TimSp, and Timb, respectively. Best subject's accuracy was 100% in all modalities and inter-stimulus intervals. Average information transfer rate for the 150ms inter-stimulus interval in the TimPiSp modality was 14.85 bits/min. Best subject's information transfer rate was 39.96 bits/min for 175ms Timbre condition. Based on the TimPiSp modality, an auditory P300 speller was implemented and evaluated by asking users to type a 12-characters-long phrase. Six out of 7 users completed the task. The average spelling speed was 0.56 chars/min and best subject's performance was 0.84 chars/min. The obtained results show that the proposed auditory BCI is successful with healthy subjects and may constitute the basis for future implementations of more practical and affordable auditory P300-based BCI systems. PeerJ PrePrints

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A P300-based brain–computer interface for people with amyotrophic lateral sclerosis

Cited by 553 publications

References 20 publications

I Think, Therefore I Am: Usability and Security of Authentication Using Brainwaves

I Think, Therefore I Am: Usability and Security of Authentication Using Brainwaves

Brain-Computer Interfaces in Medicine

A low-cost auditory multi-class brain-computer interface based on pitch, spatial and timbre cues

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