06-04-2012, 03:16 PM
3d platform for real-time neuroscince
Seminar Report.docx (Size: 966.69 KB / Downloads: 43)
Introduction
Since the pioneering work of Berger (1929), the electroencephalogram (EEG) has become a proven source of information for clinicians and researchers. First attempts to interpret EEG time series relied on visual inspection of their shape. In neurology, the morphology of EEG is stillvaluable, e.g. , in the diagnosis of epilepsy. The development of electronic devices combined with the Fast Fourier Transform (FFT) algorithm (Cooley & Tukey, 1965), allowed the analysis of the EEG spectral components and related measures (e.g., autocorrelation, coherence, etc.) initiating the era of quantitative EEG (qEEG). During the 1970s and 1980s, the introduction of micro computer technology revolutionized approaches to EEG, marking the transition from analog to digital processing. However , it has only been in the past few years that electronic technology and signal processing algorithms have become powerful enough to support the development of advanced real- time applications. EEG analysis in real-time is important for at least two reasons. First, it best exploits the high-temporal resolution of EEG, which makes the use of EEG and magneto encephalography (MEG) in real-time preferable over other neuroimaging techniques such as functional magnetic resonance imaging (fMRI). Second, it enables the provision of effective feedback to the person whose EEG is being recorded.
Implementation
Neurofeedback (EEG biofeedback) is a technique used in behavioral medicine as an adjunct to psychotherapy. An electronic device records EEG activity at a particular scalp location, extrapolates physiological measurements from the signal, and converts it to a visual and/or auditory object dynamically co-varying with the brain signal. For example, the length of a bar in a graph may vary continuously as a function of signal amplitude (smoothed in time) in one or more frequency band-pass regions. The process is truly real-time, that is, the object continuously represents brain activity with a minimum delay (<500 milliseconds). Typically , over 20 to 40 sessions of thirty minutes each, spaced two / three days apart, the subject acquires greater awareness about the signal and learns how to shape it in a desired direction, which leads to a modification of brain electrical activity.
Virtual Reality
People generally associate virtual reality to the use of sophisticated and somehow bulky interfaces such as head-mounted displays (Heilig, 1960) or data gloves (Zimmerman et al., 1987). Even researchers find it difficult to circumscribe this field and standard definitions are still subject to numerous discussions. This difficulty is a consequence of the large and heterogeneous set of tools, methods and applications used in VR. It seems that the term “Virtual Reality” was first introduced by Jaron Lanier in the 1980’s. Myron Krueger provided the first documented reference in his famous books about “ Artificial Reality” (Krueger, 1991). The SENSORAMA SIMULATOR 1 (Heilig, 1962) is 1 All names printed in capital and italic font are registered trademarks of their respective owners. Considered today as the first-ever workstation of Virtual Reality. The SENSORAMA was a whole-in- one environment, providing artificial sensations in the visual, auditory, tactile and olfactory modalities. It featured 3D video, stereo sound and vibrating seat systems.
BRAIN-COMPUTER INTERFACE
Typical computer user interfaces include a keyboard and a mouse. Research in Human - Computer Interface (HCI) has always tried to improve and to simplify the control of electronic devices. Brain-Computer Interface (BCI) aims to use a new communication channel, the activity of the brain. The goal is to achieve the so called “ think and make it happen without physical effort” paradigm (Garcia Molina et al., 2004). A typical BCI system consists of a signal acquisition device and a signal processing device. The latter outputs device - control commands. During a training phase, the participant tries repeatedly to accomplish a specific mental task. After a sufficient number of trials, given that the brain activity can be extracted in the form of a consistent, valid, and specific feature, a classification algorithm is able to translate it into a unique command.
Conclusion
In this paper we reviewed recent NF and BCI research, giving emphasis to their similarities, notably the interaction between the user and the system. We outlined some developments in VR that can be employed in NF and BCI systems to enhance their feedback capabilities. This review served as a background to introduce Open-ViBE, a general platform conceived to build brain virtual environments based upon any kind of real-time neuroimaging data. In particular, we gave an example of an EEG real-time feedback providing application. The most appreciable qualities of neurofeedback are that it is non-invasive and that it requires an active role on the part of the patient