Non-stationarities are ubiquitous in EEG signals. They are especially apparent in the use of EEG-based brain-computer interfaces (BCIs): (a) in the differences between the initial calibration measurement and the online operation of a BCI, or (b) caused by changes in the subject's brain processes during an experiment (e.g. due to fatigue, change of task involvement, etc). In this paper, we quantify for the first time such systematic evidence of statistical differences in data recorded during offline and online sessions. Furthermore, we propose novel techniques of investigating and visualizing data distributions, which are particularly useful for the analysis of (non-)stationarities. Our study shows that the brain signals used for control can change substantially from the offline calibration sessions to online control, and also within a single session. In addition to this general characterization of the signals, we propose several adaptive classification schemes and study their performance on data recorded during online experiments. An encouraging result of our study is that surprisingly simple adaptive methods in combination with an offline feature selection scheme can significantly increase BCI performance.
Learning to control brain activity: a review of the production and control of EEG components for driving brain-computer interface (BCI) systems
Boosting bit rates and error detection for the classification of fast-paced motor commands based on single-trial EEG analysis
Comparative studies on the applicability of a new surface conditioning system (Airsonic Mini Sandblaster) in adhesive bridging technic
The effect of moisture absorption on the fatigue crack propagation resistance of acrylic bone cement
Boosting bit rates in noninvasive EEG single-trial classifications by feature combination and multiclass paradigms
The BCI Competition 2003: progress and perspectives in detection and discrimination of EEG single trials
Chronomic community screening reveals about 31% depression, elevated blood pressure and infradian vascular rhythm alteration
Automatic and adaptive classification of electroencephalographic signals for brain computer interfaces
A semi-supervised support vector machine approach for parameter setting in motor imagery-based brain computer interfaces.
Translation of EEG spatial filters from resting to motor imagery using independent component analysis.
Intersession consistency of single-trial classification of the prefrontal response to mental arithmetic and the no-control state by NIRS.
Whatever works: a systematic user-centered training protocol to optimize brain-computer interfacing individually
Robust, long-term control of an electrocorticographic brain-computer interface with fixed parameters
Assisting drinking with an affordable BCI-controlled wearable robot and electrical stimulation: a preliminary investigation
Balancing a simulated inverted pendulum through motor imagery: an EEG-based real-time control paradigm
Motor imagery and action observation: modulation of sensorimotor brain rhythms during mental control of a brain-computer interface
A brain-actuated wheelchair: asynchronous and non-invasive Brain-computer interfaces for continuous control of robots
The non-invasive Berlin Brain-Computer Interface: fast acquisition of effective performance in untrained subjects
Simple adaptive sparse representation based classification schemes for EEG based brain-computer interface applications
Optimizing spatial filters by minimizing within-class dissimilarities in electroencephalogram-based brain-computer interface
Comparative analysis of spectral approaches to feature extraction for EEG-based motor imagery classification
A brain-computer interface, also known as a brain-machine interface, is a bi-directional communication pathway between an external device and a wired brain. Here is the latest research on this topic.