Biomedical Engineering Reference
In-Depth Information
the presence and the absence of the alpha rhythm.* Moreover, using feedback, they could
be trained to change the alpha rhythm's frequency. This paper deeply influenced the EEG
field and it is considered the beginning of the neurofeedback area, which influenced the
idea of using this control of brain waves to gain control of an external device.
The Advanced Research Projects Agency (ARPA, now renamed the Defense Advanced
Research Projects Agency, or DARPA) and other government agencies funded several
research projects,
†
and by the early 1970s they became interested in developing technologies
“that would permit a more immersed and intimate interaction between humans and
computers and would include so-called bionic applications” (Vidal 1999). Two projects are
worth mentioning from these years: the first directed by Dr. George Lawrence, and the
second directed by Dr. Jacques Vidal.
The first project was an internal project of ARPA. Dr. Lawrence's focus was on
autoregulation and cognitive biofeedback, aiming to develop new techniques that would
enhance human performance. Their focus was mainly on military applications because
their aim was to enhance the performance of military personnel engaged in tasks
demanding high mental loads (Wolpaw et al. 2000). This research did not produce valuable
results, although it produced some interesting insights on biofeedback. After this project,
the focus was shifted to a different approach.
At the University of California-Los Angeles (UCLA), an important project funded
by the National Science Foundation (NSF), and then by ARPA, gained attention. In
this research project, the term “brain-computer interface” was used for the first time
in a scientific article (Vidal 1973). Dr. Vidal, Director of the Brain-Computer Interface
Laboratory at UCLA, was the head of this project. One of the most interesting outcomes
of this project was to show the possibility of using single-trial visual evoked potentials
(VEPs) as a communication channel (Vidal 1977). Vidal used computer-generated visual
stimulation and sophisticated signal processing to allow a subject to control a cursor
positioned in a maze on a display. It was possible to control the cursor by a sequence of
turns in four different directions.
According to Wolpaw and colleagues (2000), Vidal highlighted the significance of
discriminating between the EEG activity and the electromyographic (EMG) activity coming
from the scalp or facial muscles. For this reason, one of the aims of the First International
Meeting on BCI was to define BCIs to exclude the muscular activity.
Over the next two decades, BCI grew as a research field, taking advantage of a better
understanding of brain processes and functions and of the new personal computer era
that brought cheap and powerful machines. Few of the current BCI applications are
actually dedicated to restore communication and movement in patients with severe
and multiple disabilities (Lebedev and Nicolelis 2006; Birbaumer et al. 2008; Daly and
Wolpaw 2008).
Several BCI software programs were developed to serve as a general platform for
different applications. These tools were generally open-source, easy to set up, and could
also be modified and adapted to new research paradigms. BCI2000 (Schalk et al. 2004;
Mellinger and Schalk 2007), BioSig (Schlögl et al. 2007), and Open-VIBE (Arrouet et al. 2005)
are examples of computer platforms that can be used to record, analyze, and translate the
EEG signal into a BCI application.
*
The alpha rhythm, discovered by Hans Berger, is an oscillation in the frequency range of 8-12 Hz. It originates
in the occipital lobe and it is usually associated with a wakeful relaxed state with closed eye
†
ARPA funded the project ARPANET, which later became what we now know as the Internet.
