The brain-computer interface (BCI) is a direct (nonmuscular) communication chan-nel between the brain and the external world that makes possible the use of neural prostheses and human augmentation. BCI interprets brain signals, such as neural spikes and cortical and scalp EEGs in an online fashion

rate above 35 bits per minute (bpm), and further two subjects above 24 and 15 bpm, while one subject could not achieve any BCI control. These results are encouraging for an EEG-based BCI system in untrained subjects that is independent of peripheral nervous system activity and does not rely on evoked

The information-transfer rate (ITR) is commonly used to assess the performance of brain-computer interfaces (BCI). Various studies have shown that the optimal number of mental tasks to be used is fairly low, around 3 or 4. We propose a formal approach and an experimental validation to demonstrate

The use of hand gestures provides an attractive alternative to cumbersome interface devices for human-computer interaction (HCI). In particular, visual interpretation of hand gestures can help in achieving the ease and naturalness desired for HCI. This has motivated a very active research area

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Abstract In many machine learning applications, like brain-computer interfaces (BCI), high-dimensional sensor array data are available. Sensor measurements are often highly correlated and signal-to-noise ratio is not homogeneously spread across sensors. Thus, collected data are highly variable

Research in Brain-Computer Interface (BCI) has significantly increased during the last few years. In addition to their initial role as assisting devices for the physically challenged, BCIs are now proposed for a wider range of applications. As in any HCI application, BCIs can also benefit from

There has been much interest recently in the concept of using information from the motor cortex region of the brain, recorded using non-invasive scalp electrodes, to construct a crude interface with a computer. It is known that movements of the limbs, for example, are accompanied

Most EEG-based BCI systems make use of well-studied patterns of brain activity. However, those systems involve tasks that indirectly map to simple binary commands such as “yes” or “no” or require many weeks of biofeedback training. We hypothesized that signal processing and machine learning methods

Abstract P300 brain–computer interface (BCI) systems typically use a row/column (RC) approach. This article presents a P300 BCI based on a 12 9 7 matrix and new paradigmatic approaches to flashing characters designed to decrease the number of flashes needed to identify a target character. Using