N200-speller using motion-onset visual response

Hong, Bo; Guo, Fei; Liu, Tao; Gao, Xiaorong; Gao, Shangkai

doi:10.1016/j.clinph.2009.06.026

Cited by 179 publications

(154 citation statements)

References 40 publications

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“…The computer identifies the attended item as the intersection of the row and column that elicited the largest P300. The RCP has been tested in various configurations to achieve efficient communication that is practical for in-home use Lenhardt et al, 2008;; the paradigm itself has been modified (Guger et al, 2009;Hong et al, 2009;Martens et al, 2009;Salvaris & Sepulveda, 2009;Takano et al, 2009). Unfortunately, none of such alternative paradigms substantially improves P300-based BCI performance.…”

Section: Treatment Of Impaired Communicationmentioning

confidence: 99%

Communication Impairment in ALS Patients Assessment and Treatment

Bongioanni¹

2012

Amyotrophic Lateral Sclerosis

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Section: Treatment Of Impaired Communicationmentioning

confidence: 99%

Communication Impairment in ALS Patients Assessment and Treatment

Bongioanni¹

2012

Amyotrophic Lateral Sclerosis

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“…Among the different approaches used in BCI studies, those based on event related potentials (ERPs) [11][12][13] are rather appealing since, at least in principle, they offer a relatively high bit-rate and require no user training. ERPs are relatively well defined shape-wise variations of the ongoing EEG which are elicited by a stimulus and are temporally linked to it.…”

Section: P300 Waves and Factors Affecting Their Characteristicsmentioning

confidence: 99%

Documenting, modelling and exploiting P300 amplitude changes due to variable target delays in Donchin's speller

2010

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Abstract. The P300 is an endogenous Event-Related Potential (ERP) which is naturally elicited by rare and significant external stimuli. P300s are used increasingly frequently in Brain Computer Interfaces (BCI) because users of ERP-based BCIs need no special training. However, P300 waves are hard to detect and, therefore, multiple target stimulus presentations are needed before an interface can make a reliable decision. While significant improvements have been made in the detection of P300s, no particular attention has been paid to the variability in shape and timing of P300 waves in BCIs. In this paper we start filling this gap by documenting, modelling and exploiting a modulation in the amplitude of P300s related to the number of non-targets preceding a target in a Donchin speller. The basic idea in our approach is to use an appropriately weighted average of the responses produced by a classifier during multiple stimulus presentations, instead of the traditional plain average. This makes it possible to weigh more heavily events that are likely to be more informative, thereby increasing the accuracy of classification. The optimal weights are determined through a mathematical model that precisely estimates the accuracy of our speller as well as the expected performance improvement w.r.t. the traditional approach. Tests with two independent datasets show that our approach provides a marked statistically significant improvement in accuracy over the top performing algorithm presented in the literature to date. The method and the theoretical models we propose are general and can easily be used in other P300-based BCIs with minimal changes.

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“…One typical example is the work of Tanaka et al, who first developed a brain-controlled robotic wheelchair whose left or right turning movements are directly controlled by corresponding motion commands translated from user brain signals while imagining left or right limb movements, and tested this system in real-world situations [2], [3]. However, the overall performance of these braincontrolled mobile robots mainly depends on the performance of the noninvasive BCIs, which are currently slow and uncertain [4], [5]. In other words, the performance of the BCI systems limits that of the robots.…”

Section: Introductionmentioning

confidence: 99%

Semi Autonomous Hybrid Brain Machine Interface

Babu¹

2012

IJAREEIE

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This paper discusses about a brain controlled robot based on Brain-computer interfaces (BCI). BCIs are systems that can bypass conventional channels of communication (i.e., muscles and thoughts) to provide direct communication and control between the human brain and physical devices by translating different patterns of brain activity into commands in real time. With these commands a mobile robot can be controlled. The intention of this paper is to develop a robot that can assist the disabled people in their daily life to do some work independent on others. Brain signals will be sensed by the brain wave sensor and it will convert the data into packets and transmit through Bluetooth medium. Level analyzer unit (LAU) will receive the brain wave raw data and it will extract and process the signal using Matlab platform. Then the control commands will be transmitted to the robotic ARM module to process. With this entire system, we can pick an object and place it accordingly through the configured brain signals.

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N200-speller using motion-onset visual response

Cited by 179 publications

References 40 publications

Communication Impairment in ALS Patients Assessment and Treatment

Communication Impairment in ALS Patients Assessment and Treatment

Documenting, modelling and exploiting P300 amplitude changes due to variable target delays in Donchin's speller

Semi Autonomous Hybrid Brain Machine Interface

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