Advances in Non-Invasive Brain-Computer Interfaces for Control and Biometry

During the last decade many advances in a number of fields have supported the idea that a direct interface between the human brain and an artificial system, called Brain Computer Interface (BCI), is a viable concept, although a significant research and development effort has to be conducted before these technologies enter routine use. The conceptual approach is to model the brain activity variations and map them into some kind of actuation over a target output through the use of signal processing and machine learning methods. In the meantime, several working BCI systems have been described in the literature using a variety of signal acquisition methods, experimental paradigms, pattern recognition approaches and output interfaces, and requiring different types of cognitive activity (Allison et al., 2008; Bashashati et al., 2007; Berger et al., 2008; Leeb et al.‚ 2007; Millán, 2008; Müller-Putz & Pfurtscheller‚ 2008). Nowadays, the principal reason for the BCI research is the potential benefits to those with severe motor disabilities, such as brainstem stroke, amyotrophic lateral sclerosis or severe cerebral palsy (Bensch et al.‚ 2007; Birbaumer et al.‚ 2007; Nijboer et al.‚ 2008; Pfurtscheller et al.‚ 2008). However, the most recent advances in acquisition technology and signal processing assert that controlling certain functions by neural interfaces may have a significant impact in the way people will operate computers, wheelchairs, prostheses, robotic systems and other devices. A very effective way to analyze the brain physiological activity is the electroencephalogram (EEG) measurements from the cortex whose sources are the action potentials of the nerve cells in the brain. The theoretical and the application studies are based on the knowledge that the EEG signals are composed of waves inside the 0-60 Hz frequency band and on the fact that different brain activities can be identified based on the recorded oscillations (Niedermayer & Lopes da Silva, 1999). Over the last years, the interest in extracting knowledge hidden in the EEG signals is rapidly growing, as well as their applications. EEGbased BCIs for motor control and biometry are among the most recent applications in the computational neuro-engineering field. Despite the proof of concept and many encouraging results achieved by some research groups (Marcel & Millán, 2007; Millán et al.‚ 2004; Palaniappan & Mandic, 2007; Pineda, 2005; Pfurtscheller et al., 2006; Vidaurre et al., 2006), additional efforts are required in order to design and implement efficient BCIs. For example, reliable signal processing and pattern recognition techniques able to continuously extract meaningful information from the very noisy EEG is still a high challenge.