Advances in Neural Signal Processing
4
population of neurons around the local neighborhood. Thus, applying appropriate feature extraction methods can isolate and extract significant features in both
temporal and spatial domains.
3.2.1 Spatial filtering
For some methods that record brain signals using multi-electrodes, the signals are
recorded from multiple regions of the brain. With the large variance of global noise,
the local signals appear diminished. Therefore, spatial filtering or re-referencing
methods are applied to enhance the local activity and filter out the common noise. For
individual electrodes, the averaged activity from surrounded electrodes (Laplacian
filtering) or from global electrodes (common averaged referencing) is subtracted.
Spatial filtering methods can also be used to estimate the variance of the neural data.
3.2.2 Temporal analysis
The quality of the recorded brain signals primarily depends on recording
techniques. However, the recorded time-series signals contain lots of noise that can
be filtered using time-domain filtering methods. Numerous filtering techniques like
moving average smoothing, exponential smoothing, etc. are used to preprocess raw
signals in the time domain.
In addition to filtering, temporal analysis can also be used to extract significant
features that represent behavior. These significant features can be extracted out
from a series of time signals using computational models. Some neural signals tend
to be correlated over time, and thus, the following time samples are possible to be
predicted based on the previous samples using autoregressive models (for stationary signals) or adaptive autoregressive models (for nonstationary signals). Such
methods depend on the model built up from the characteristic internal relationships
between the previous signal samples and the subsequent samples. The coefficients
of the model can be considered as neural features for the subsequent pattern recognition or classification procedure utilized for real-time decoding or estimation.
3.2.3 Frequency analysis
While temporal analysis methods are useful, there are some signals for which
these methods may not result in extracting meaningful features. For example,
noninvasive methods such as EEG are based on signals that reflect the activity of
several thousands of neurons. Poor spatial and temporal resolution challenges the
feature extraction in the time domain. The recorded signal thus can capture only
the correlated activities of large populations of neurons, such as oscillatory activity.
The intrinsic property of the brain signals is neuronal oscillations [3]. Theoretically,
these oscillations can be decomposed with a set of basis functions, such as sinusoid
functions using Fourier transform (FT) for periodic signals. For each cycle, the
amplitude, the period, and the waveform symmetry are measured and oscillatory
bursts are algorithmically identified, allowing us to investigate the variability of
oscillatory features within and between bursts. Usually, for neural signals, short-time
Fourier transform (STFT) provides better results by performing FT with sliding
short-time windows. For the nonperiodic signals, the wavelet transform is applied for
signal decomposition. A variety of scaled and finite-length waveforms can be selected
according to the shape of the raw neural signals. The wavelet coefficients sometimes
contain unique information which can be considered as neural features. Additionally,
the power spectrum of neural signals usually reflects lots is important information,
such as power spectral density (PSD).
4
population of neurons around the local neighborhood. Thus, applying appropriate feature extraction methods can isolate and extract significant features in both
temporal and spatial domains.
3.2.1 Spatial filtering
For some methods that record brain signals using multi-electrodes, the signals are
recorded from multiple regions of the brain. With the large variance of global noise,
the local signals appear diminished. Therefore, spatial filtering or re-referencing
methods are applied to enhance the local activity and filter out the common noise. For
individual electrodes, the averaged activity from surrounded electrodes (Laplacian
filtering) or from global electrodes (common averaged referencing) is subtracted.
Spatial filtering methods can also be used to estimate the variance of the neural data.
3.2.2 Temporal analysis
The quality of the recorded brain signals primarily depends on recording
techniques. However, the recorded time-series signals contain lots of noise that can
be filtered using time-domain filtering methods. Numerous filtering techniques like
moving average smoothing, exponential smoothing, etc. are used to preprocess raw
signals in the time domain.
In addition to filtering, temporal analysis can also be used to extract significant
features that represent behavior. These significant features can be extracted out
from a series of time signals using computational models. Some neural signals tend
to be correlated over time, and thus, the following time samples are possible to be
predicted based on the previous samples using autoregressive models (for stationary signals) or adaptive autoregressive models (for nonstationary signals). Such
methods depend on the model built up from the characteristic internal relationships
between the previous signal samples and the subsequent samples. The coefficients
of the model can be considered as neural features for the subsequent pattern recognition or classification procedure utilized for real-time decoding or estimation.
3.2.3 Frequency analysis
While temporal analysis methods are useful, there are some signals for which
these methods may not result in extracting meaningful features. For example,
noninvasive methods such as EEG are based on signals that reflect the activity of
several thousands of neurons. Poor spatial and temporal resolution challenges the
feature extraction in the time domain. The recorded signal thus can capture only
the correlated activities of large populations of neurons, such as oscillatory activity.
The intrinsic property of the brain signals is neuronal oscillations [3]. Theoretically,
these oscillations can be decomposed with a set of basis functions, such as sinusoid
functions using Fourier transform (FT) for periodic signals. For each cycle, the
amplitude, the period, and the waveform symmetry are measured and oscillatory
bursts are algorithmically identified, allowing us to investigate the variability of
oscillatory features within and between bursts. Usually, for neural signals, short-time
Fourier transform (STFT) provides better results by performing FT with sliding
short-time windows. For the nonperiodic signals, the wavelet transform is applied for
signal decomposition. A variety of scaled and finite-length waveforms can be selected
according to the shape of the raw neural signals. The wavelet coefficients sometimes
contain unique information which can be considered as neural features. Additionally,
the power spectrum of neural signals usually reflects lots is important information,
such as power spectral density (PSD).