578
R. B. Pachori and V. Gupta
The main reason behind the use of MEG signal over EEG signal is that the EEG
signals have significant noise because of the muscles’ electrical activities being very
close to the electrodes, whereas the MEG signal can record from DC to very high
frequency (>600) without skin contact. The MEG signal is also capable of detecting
neuronal electrical activities from deep inside the brain, while the neuronal electrical
activities close to surface of the brain are often captured by EEG signals. Moreover,
the MEG signal has less distorted signals which provide much better spatial and
temporal representation of the brain. A major advantage of MEG signal is that it
can provide an exact location and timing of cortical generators for event-related
responses and spontaneous brain oscillations. The MEG signal provides a spatial
accuracy of a few millimeters along with submillisecond accurate temporal resolution under optimal conditions. These accurate configurations provide the much
effective spatiotemporal tracking of distributed resolution in case of cognitive tasks
or epileptic discharges. The weak magnetic field in MEG signal recording machine
is sensed by large superconducting quantum interference devices (SQUIDs). The
SQUID sensors are able to deliver both natural and evoked physiological responses
in MEG signal due to weak strength of magnetic field which is about picotesla (pT).
The interference present in MEG signal is mainly due to earth’s magnetic field and
this interference can be filtered by the MEG signal recording machine. The analysis
of MEG signals is possible in a similar way as the EEG signals due to resemblance.
Thus, the same processing techniques which are used for EEG signals can be utilized
for MEG signals [30].
The MEG signals of left, right, forward, and backward movements from subject
S01 are shown in Fig. 12.34, respectively. These signals are obtained from BCI
competition IV dataset 3 [32]. The dataset contains ten channels of MEG signals,
namely, LC21, LC22, LC23, LC31, LC32, LC41, LC42, RC41, ZC01, and ZC02.
These signals are recorded with two subjects S01 and S02 with a sampling frequency
of 400 Hz. A total of 400 samples are present in a signal resulting in a 1-s time
duration.
12.4.6 Other Biomedical Signals
Biomedical signals are not limited to the abovementioned category of signals.
There are many other biomedical signals which are used for clinical and research
purpose. The signals which are used for the diagnosis of heart sounds are known
as phonocardiogram (PCG) signals. In PCG signals, the heart sounds are observed
during the inside and outside flow of the blood in the heart compartments. These
signals are often recoded with the help of mechanical stethoscopes which amplify
the heart sounds. However, the mechanical stethoscopes have an uneven frequency
response and this frequency response distorted the heart sound signals. Thus, an
electronic stethoscope can overcome this problem and provide a less distorted heart
sound signal.
R. B. Pachori and V. Gupta
The main reason behind the use of MEG signal over EEG signal is that the EEG
signals have significant noise because of the muscles’ electrical activities being very
close to the electrodes, whereas the MEG signal can record from DC to very high
frequency (>600) without skin contact. The MEG signal is also capable of detecting
neuronal electrical activities from deep inside the brain, while the neuronal electrical
activities close to surface of the brain are often captured by EEG signals. Moreover,
the MEG signal has less distorted signals which provide much better spatial and
temporal representation of the brain. A major advantage of MEG signal is that it
can provide an exact location and timing of cortical generators for event-related
responses and spontaneous brain oscillations. The MEG signal provides a spatial
accuracy of a few millimeters along with submillisecond accurate temporal resolution under optimal conditions. These accurate configurations provide the much
effective spatiotemporal tracking of distributed resolution in case of cognitive tasks
or epileptic discharges. The weak magnetic field in MEG signal recording machine
is sensed by large superconducting quantum interference devices (SQUIDs). The
SQUID sensors are able to deliver both natural and evoked physiological responses
in MEG signal due to weak strength of magnetic field which is about picotesla (pT).
The interference present in MEG signal is mainly due to earth’s magnetic field and
this interference can be filtered by the MEG signal recording machine. The analysis
of MEG signals is possible in a similar way as the EEG signals due to resemblance.
Thus, the same processing techniques which are used for EEG signals can be utilized
for MEG signals [30].
The MEG signals of left, right, forward, and backward movements from subject
S01 are shown in Fig. 12.34, respectively. These signals are obtained from BCI
competition IV dataset 3 [32]. The dataset contains ten channels of MEG signals,
namely, LC21, LC22, LC23, LC31, LC32, LC41, LC42, RC41, ZC01, and ZC02.
These signals are recorded with two subjects S01 and S02 with a sampling frequency
of 400 Hz. A total of 400 samples are present in a signal resulting in a 1-s time
duration.
12.4.6 Other Biomedical Signals
Biomedical signals are not limited to the abovementioned category of signals.
There are many other biomedical signals which are used for clinical and research
purpose. The signals which are used for the diagnosis of heart sounds are known
as phonocardiogram (PCG) signals. In PCG signals, the heart sounds are observed
during the inside and outside flow of the blood in the heart compartments. These
signals are often recoded with the help of mechanical stethoscopes which amplify
the heart sounds. However, the mechanical stethoscopes have an uneven frequency
response and this frequency response distorted the heart sound signals. Thus, an
electronic stethoscope can overcome this problem and provide a less distorted heart
sound signal.