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In this paper, the use of mutual information and the Learn++.NSE algorithm is proposed to create an EEG SSVEP BCI system that can select and utilize data sets originating from a group of users. In typical BCI systems, the nonstationarity in the EEG prevents the system from blindly applying training data from other users to the incoming data. Mutual information is introduced to select previous data...
Brain-computer interfaces (BCIs) promise to promote a novel access channel for functional independence for individuals with severe speech and physical impairment (SSPI) that can occur as a result of numerous neurological diseases and injuries. Current BCI systems lack the robustness and accuracy to allow individuals with SSPI to complete tasks required for independent living (e.g. communication or...
Recent advances in signal processing for the detection of Steady-State Visual Evoked Potentials (SSVEPs) have moved away from traditionally calibrationless methods, such as canonical correlation analysis, and towards algorithms that require substantial training data. In general, this has improved detection rates, but SSVEP-based brain-computer interfaces (BCIs) now suffer from the requirement of costly...
Despite their potential for many applications, Brain -- Computer Interfaces (BCI) are still rarely used due to their low reliability and long training. These limitations are partly due to inappropriate training protocols, which includes the feedback provided to the user. While feedback should theoretically be explanatory, motivating and meaningful, current BCI feedback is usually boring, corrective...
Goal: Motor imagery-related mu/beta rhythms, which can be voluntarily modulated by subjects, have been widely used in EEG-based brain computer interfaces (BCIs). Moreover, it has been suggested that motor imagery-specific EEG differences can be enhanced by feedback training. However, the differences observed in the EEGs of naive subjects are typically not sufficient to provide reliable EEG control...
In this paper, a novel method for detecting steadystate visual evoked potentials (SSVEP) using multiple channel electroencephalogram (EEG) data is presented. Accurate asynchronous detection, high speed and high information transfer rate can be achieved after a short calibration session. Spatial filtering based on the Canonical Corelation Analysis method proposed in [1] is used for identifying optimal...
Visually stimulated brain-computer interfacing detects which target on a screen a user is gazing at; however, this is also accomplished by tracking gaze points with a camera. These two approaches have been independently investigated and sometimes doubts about BCI with visual stimuli are raised in terms of usability compared to eye tracking interfaces (ETI). This paper answers this question by investigating...
Successful brain-computer interfaces (BCIs) swiftly and accurately communicate the user's intention to a computer. Typically, information transfer rate (ITR) is used to measure the performance of a BCI. We propose a multi-step process to speed up detection and classification of the user's intent and maximize ITR. Users randomly looked at 4 frequency options on the interface in two sessions, one without...
This work evaluates a possibility of creating a high-frequency, SSVEP-based brain computer interface using a low cost EEG recording hardware — an Emotiv EEG Neuro-headset. Both above aspects are crucial to enable deploying the BCI technology in the consumer market. High frequencies can be used to create a non-tiring and more pleasant interface. Commercial EEG systems, as the Emotiv EEG, although demonstrating...
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