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be those that have lost most or all motor functions (known as
syndrome)
due to progressive neuromuscular diseases like amyotrophic lateral sclerosis (ALS)
or muscular dystrophy or non-progressive such as stroke, traumatic brain injury and
spinal cord injury. The BCI approaches for such individuals could be used in
control of wheelchair, prosthesis, basic communication etc., as shown in Fig.
2.1
.
These users could use BCI to communicate with others to express their needs,
feelings, etc. A simple example could be of a communication BCI system such as
brain controlled word processing software.
However, in recent years, other industries have taken interest in this
'
locked in
'
field where
applications related to biometrics (Palaniappan
2008
), games (Hasan and Gan
2012
), cursor control (Wilson and Palaniappan
2011
) etc., have emerged. Table
2.1
gives a non-exhaustive list of possible applications of BCI for both disabled and
healthy individuals.
In general, there are two categories of BCI: invasive and non-invasive methods.
Invasive BCI methods such as electrocorticogram (ECoG) have shown excellent
performance in human (Langhenhove et al.
2008
) and monkey (Borton et al.
2013
).
Nevertheless, non-invasive approaches based on electroencephalogram (EEG),
magnetoencephalogram (MEG), positron emission topography (PET), functional
magnetic resonance imaging (fMRI) and near-infrared spectroscopy (NIRs) are
more popular as it is safer (minimal risk of infection etc.).
Among these non-invasive methods, EEG-based BCI is preferred due to it being
practical (i.e. cheap and portable). We will focus on EEG-based BCI techniques
here, speci
cally on transient visual evoked potential (better known as P300), motor
imagery, steady-state visual evoked potential (SSVEP), mental tasks and brie
yon
slow cortical potential (SCP). Figure
2.2
shows a block diagram of the components
involved in the processing of EEG data to implement a BCI.
Fig. 2.1
Brain
computer interface applications
-
