Machine Learning Methods of the Berlin Brain-Computer Interface

Machine Learning Methods of the Berlin Brain-Computer Interface

This paper is a compilation of the most recent machine learning methods used in the Berlin Brain-Computer Interface. In the field of Brain-Computer Interfacing, machine learning has been mainly used to extract meaningful features from noisy signals of large dimensionality and to classify them to transform them into computer commands. Recently, our group developed different methods to deal with ...

The Berlin Brain-Computer Interface: Machine Learning Based Detection of User Specific Brain States

We outline the Berlin Brain-Computer Interface (BBCI), a system which enables us to translate brain signals from movements or movement intentions into control commands. The main contribution of the BBCI, which is a non-invasive EEG-based BCI system, is the use of advanced machine learning techniques that allow to adapt to the specific brain signatures of each user with literally no training. In...

Brain Computer Interface using Machine Learning

This paper presents the design and development of a complete hardware and software solution for a brain computer interface (BCI). It consists of a non-intrusive multiple channel data acquisition device which captures the electrical brain wave signals and passes the data to a computer. The computer then uses signal processing and machine learning algorithms to identify patterns in the signals re...

Advanced Human-Computer Interaction with the Berlin Brain-Computer Interface

Brain-Computer Interfaces (BCIs) are systems capable of decoding neural activity in real time, thereby allowing a computer application to be directly controlled by the brain. Since the characteristics of such direct brain-to-computer interaction are limited in several aspects, one major challenge in BCI research is intelligent front-end design. Here we present the mental text entry application ...

Machine learning methods for brain computer interfaces