Effects of text generation on P300 brain-computer interface performance
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Abstract:
Brain-computer interfaces (BCIs) are intended to provide independent communication for those with the most severe physical impairments. However, development and testing of BCIs is typically conducted with copy-spelling of provided text, which models only a small portion of a functional communication task. This study was designed to determine how BCI performance is affected by novel text generation. We used a within-subject single-session study design in which subjects used a BCI to perform copy-spelling of provided text and to generate self-composed text to describe a picture. Additional off-line analysis was performed to identify changes in the event-related potentials that the BCI detects and to examine the effects of training the BCI classifier on task-specific data. Accuracy was reduced during the picture description task; (t(8)=2.59 p=0.0321). Creating the classifier using self-generated text data significantly improved accuracy on these data; (t(7)=-2.68, p=0.0317), but did not bring performance up to the level achieved during copy-spelling. Thus, this study shows that the task for which the BCI is used makes a difference in BCI accuracy. Task-specific BCI classifiers are a first step to counteract this effect, but additional study is needed.Keywords:
Spelling
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Objective: Brain-computer interface (BCI) spelling is a promising communication solution for people in paralysis. Currently, BCIs suffer from imperfect decoding accuracy which calls for methods to handle spelling mistakes. Detecting error-related potentials (ErrPs) has been early identified as a potential remedy. Nevertheless, few works have studied the elicitation of ErrPs during engagement with other BCI tasks, especially when BCI feedback is provided continuously. Methods: Here, we test the possibility of correcting errors during pseudo-online Motor Imagery (MI) BCI spelling through ErrPs, and investigate whether BCI feedback hinders their generation. Ten subjects performed a series of MI spelling tasks with and without observing BCI feedback. Results: The average pseudo-online ErrP detection accuracy was found to be significantly above the chance level in both conditions and did not significantly differ between the two (74% with, and 78% without feedback). Conclusions: Our results support the possibility to detect ErrPs during MI-BCI spelling and suggest the absence of any BCI feedback-related interference.
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Brain-signal has been studied as an input modality for human-machine interface (HMI). Using Brain-signal-based HMI, people with impaired abilities can communicate with a machine by the brain's electrical activity. This study focused on Electro Encephalo Gram (EEG) signal measurement and analysis methods related to concentration for multimodal Interface. The experiments have been performed with various tasks. The result of this study showed there are some meaningful results about threshold, selfarithmetic and eye-close activity. It can be used as a modality for brain-achine interface.
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This study proposes a novel hybrid brain-computer interface (BCI) approach for increasing the spelling speed. In this approach, the P300 and steady-state visually evoked potential (SSVEP) detection mechanisms are devised and integrated so that the two brain signals can be used for spelling simultaneously. Specifically, the target item is identified by 2-D coordinates that are realized by the two brain patterns. The subarea/location and row/column speedy spelling paradigms were designed based on this approach. The results obtained for 14 healthy subjects demonstrate that the average online practical information transfer rate, including the time of break between selections and error correcting, achieved using our approach was 53.06 bits/min. The pilot studies suggest that our BCI approach could achieve higher spelling speed compared with the conventional P300 and SSVEP spellers.
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Brain Computer Interface (BCI) is a device that enables use of brain’s neural activity to communicate with others or other machines without direct physical contact. Brain Computer interface was initially used to help people affected by motor neuron diseases. BCI is accomplished through Electroencephalography (EEG) that measures the electrical activity produced by the neurons in the brain. The existing BCI solutions are really expensive and complex so we wanted to find a way to build a simple and cost effective BCI. A single channel EEG acquisition system was designed and implemented on few healthy subjects. We used Support Vector Machine algorithm to train and classify the signals obtained from the subjects and obtained an accuracy of 70%.
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Brain Computer Interface consists of hardware and software that convert brain signals into action. It changes the nerves, muscles, and movements they produce with electro-physiological signs. The BCI cannot read the brain and decipher the thought in general. The BCI can only identify and classify specific patterns of activity in ongoing brain signals associated with specific tasks or events. EEG is the most commonly used non-invasive BCI method as it can be obtained easily compared to other methods. In this study; It will be given how EEG signals are obtained from the scalp, with which waves these frequencies are named and in which brain states these waves occur. 10-20 electrode placement plan for EEG to be placed on the scalp will be shown.
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Auditory-evoked noninvasive electroencephalography (EEG) based brain-computer interfaces (BCIs) could be useful for improved hearing aids in the future. This manuscript investigates the role of frequency and spatial features of audio signal in EEG activities in an auditory BCI system with the purpose of detecting the attended auditory source in a cocktail party setting. A cross correlation based feature between EEG and speech envelope is shown to be useful to discriminate attention in the case of two different speakers. Results indicate that, on average, for speaker and direction (of arrival) of audio signals classification, the presented approach yields 91% and 86% accuracy, respectively.
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A brain-computer interface (BCI) enables direct communication from the brain to devices, bypassing the traditional pathway of peripheral nerves and muscles. Traditional approaches to BCIs require the user to train for weeks or even months to learn to control the BCI. In contrast, BCIs based on machine learning only require a calibration session of less than an hour before the system can be used, since the machine adapts to the user's existing brain signals. However, this calibration session has to be repeated before each use of the BCI due to inter-session variability, which makes using a BCI still a time-consuming and an error-prone enterprise. In this work, we present a second-order baselining procedure that reduces these variations, and enables the creation of a BCI that can be applied to new subjects without such a calibration session. The method was validated with a motor-imagery classification task performed by 109 subjects. Results showed that our subject-independent BCI without calibration performs as well as the popular common spatial patterns (CSP)-based BCI that does use a calibration session.
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Lack of communication causes problems for patients with neurodegenerative diseases, so the need for alternative methods is required to convey their thoughts with caretakers, friends, and family members. Brain-computer interface (BCI) is a device to control external devices by using mental thoughts without any other muscle movements to improve the communication quality for the disabled individual without any other help. The techniques of measuring electrical signal around the scalp during some activities by using electrodes are called electroencephalogram (EEG). By combining these two technologies together to form a brain-computer interaction, this helps the paralyzed individual to communicate with others to share the thoughts. In this paper, we deal with the history of EEG, electrode placements with measurements, and signal ranges, and also further discussed some of the prominent studies completed in designing BCI using EEG. This helps the new researchers to know the EEG measurement and position completely and paved the new way to create EEG-based interface research.
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Brain-Computer Interfaces (BCI) are systems that can translate the brain activity patterns of a user into messages or commands for an interactive application. The brain activity which is processed by the BCI systems is usually measured using Electroencephalography (EEG). The BCI system uses oscillatory Electroencephalography (EEG) signals, recorded using specific mental activity, as input and provides a control option by its output. A brain-computer interface uses electrophysiological signals to control the remote devices. They consist of electrodes applied to the scalp of an individual or worn in an electrode cap. The computer processes the EEG signals and uses it in order to accomplish tasks such as communication and environmental control.
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BRAIN computer interface (BCI) is a communication technique that aims to detect and identify brain intents and translate them into machine commands to control the operation of electrical and/or mechanical devices. Electroencephalography (EEG) is a widely used imaging technique for noninvasive BCI. Due to EEG non-stationarity, which is typically caused by variation of head size, electrode positions and/or impedance, subjects' mind states, eye or muscular movements, EEG signals exhibit significant inter-subject variation. As a result, a BCI system trained from a subject may not be directly applicable to others, and a significant amount of time is required to re-calibrate the BCI system to a new subject. This inefficiency is one of the major challenges in EEG-based BCI systems. The goal of this work is to address the multisubject BCI classification by evaluating a set of EEG features and identifying those showing higher stationarity than others.
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