Ongoing EEG artifact correction using blind source separation.

OBJECTIVE: Analysis of the electroencephalogram (EEG) for epileptic spike and seizure detection or brain-computer interfaces can be severely hampered by the presence of artifacts. The aim of this study is to describe and evaluate a fast automatic algorithm for ongoing correction of artifacts in continuous EEG recordings, which can be applied offline and online. METHODS: The automatic algorithm for ongoing correction of artifacts is based on fast blind source separation. It uses a sliding window technique with overlapping epochs and features in the spatial, temporal and frequency domain to detect and correct ocular, cardiac, muscle and powerline artifacts. RESULTS: The approach was validated in an independent evaluation study on publicly available continuous EEG data with 2035 marked artifacts. Validation confirmed that 88% of the artifacts could be removed successfully (ocular: 81%, cardiac: 84%, muscle: 98%, powerline: 100%). It outperformed state-of-the-art algorithms both in terms of artifact reduction rates and computation time. CONCLUSIONS: Fast ongoing artifact correction successfully removed a good proportion of artifacts, while preserving most of the EEG signals. SIGNIFICANCE: The presented algorithm may be useful for ongoing correction of artifacts, e.g., in online systems for epileptic spike and seizure detection or brain-computer interfaces.

Touch interface for sensing fingertip force in mobile device using electromyogram.

The aim of this study is to develop a three-dimensional touch interface for mobile devices, specifically a touch interface for detecting fingertip force. This interface consists of a conventional touch interface and an electromyogram (EMG) amplifier. The fingertip force during manipulation of the touch interface is estimated from the EMG measurement. We develop a method for obtaining fingertip force information using an EMG, while the two-dimensional position of the finger is measured using the conventional touch interface found in mobile devices. Further, we evaluate the validity of our newly developed interface by comparing the fingertip force estimated using our proposed method with the fingertip force measured using a force sensor. Lastly, we develop an application using our interface.