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OBJECTIVE A fully automated method for reducing EOG artifacts is presented and validated. METHODS The correction method is based on regression analysis and was applied to 18 recordings with 22 channels and approx. 6 min each. Two independent experts scored the original and corrected EEG in a blinded evaluation. RESULTS The expert scorers identified in(More)
In recent years, new research has brought the field of electroencephalogram (EEG)-based brain-computer interfacing (BCI) out of its infancy and into a phase of relative maturity through many demonstrated prototypes such as brain-controlled wheelchairs, keyboards, and computer games. With this proof-of-concept phase in the past, the time is now ripe to focus(More)
The aim of the present study was to demonstrate for the first time that brain waves can be used by a tetraplegic to control movements of his wheelchair in virtual reality (VR). In this case study, the spinal cord injured (SCI) subject was able to generate bursts of beta oscillations in the electroencephalogram (EEG) by imagination of movements of his(More)
The step away from a synchronized or cue-based brain-computer interface (BCI) and from laboratory conditions towards real world applications is very important and crucial in BCI research. This work shows that ten naive subjects can be trained in a synchronous paradigm within three sessions to navigate freely through a virtual apartment, whereby at every(More)
Brain-Computer Interface (BCI) research has become a growing field of interest in the last years. The work presented ranges from machine learning approaches in offline results to the application of a BCI in patients. However, reliable classification of brain activity is a crucial issue in BCI research. In contrast to most articles which present methods to(More)
This paper compares different ICA preprocessing algorithms on cross-validated training data as well as on unseen test data. The EEG data were recorded from 22 electrodes placed over the whole scalp during motor imagery tasks consisting of four different classes, namely the imagination of right hand, left hand, foot and tongue movements. Two sessions on(More)
The self-paced control paradigm enables users to operate brain-computer interfaces (BCI) in a more natural way: no longer is the machine in control of the timing and speed of communication, but rather the user is. This is important to enhance the usability, flexibility, and response time of a BCI. In this work, we show how subjects, after performing(More)
Online analysis and classification of single electroencephalogram (EEG) trials during motor imagery were used for navigation in the virtual environment (VE). The EEG was recorded bipolarly with electrode placement over the hand and foot representation areas. The aim of the study was to demonstrate for the first time that it is possible to move through a(More)