5
Introductory Chapter: Methods and Applications of Neural Signal Processing
DOI: http://dx.doi.org/10.5772/intechopen.93335
3.2.4 Time-frequency analysis
By combining the advantages of temporal and frequency analyses, the researchers realized the power of time-frequency analysis. As an example, using decomposition techniques, a signal can be decomposed into intrinsic mode functions (IMF)
and instantaneous frequencies over time can be obtained by applying methods such
as Hilbert spectral analysis. The most significant advantage of this technique is that
the nonlinear, nonstationary recorded neural signals can be transformed into linear
and stationary components. These components are usually physically meaningful since the special features are localized in their instantaneous frequencies and
represent meaningful behavioral information in the time-frequency domain. Timefrequency analysis is extensively implemented in neural signal processing since the
individual analysis in the time or frequency domain comes with respective disadvantages, and time-frequency analysis trades off time and frequency resolution to
get the best representation of the signals. Other techniques such as spectrogram and
STFT are most performed by segmenting a signal into short periods and estimating
the spectrum over sliding windows.
3.3 Dimensionality reduction
A critical procedure in neural signal processing is to reduce the high dimensionality of the recorded neural data. These data could be brain images, multi-electrode
signals, network potentials, or high-dimensional neural features. Several algorithms
can be applied linearly or nonlinearly to preserve the most useful components and
remove redundancies. Principal component analysis (PCA) is to find the direction of maximum variance and thereby build the principal components (weighted
linear combinations) based on the observed variance. Linear discriminant analysis
(LDA) performs similar to PCA but tends to minimize the variance within a group
of neural data and maximize the distance between groups of neural data. Thus,
PCA is described as an unsupervised algorithm implemented for feature extraction, and LDA is described as a supervised algorithm that uses training based on
labels for groups of data. Other methods that are used most often are CCA and
ICA. Canonical correlation analysis (CCA) is yet another method for exploring the
relationships between two multivariate sets of variables allowing us to summarize
the relationships into a lesser number of variables while preserving the essential
features of the relationships. Independent component analysis (ICA) is a blind
source separation method rather than a dimensionality reduction method. Neural
signals consist of recordings of potentials that are presumably generated by mixing
some underlying components of brain activity. ICA can theoretically isolate these
underlying components of brain activity by computing independent components.
Additionally, ICA can also be used as a filtering method to remove signal artifacts
generated by eye blinks or other artifacts in EEG signals.
3.4 Machine learning algorithms
Machine learning or deep learning algorithms have become increasingly popular
and are being implemented in many fields. They can be broadly divided into unsupervised learning and supervised learning. Unsupervised learning methods aim to
extract hidden structures within the neural data, commonly used for feature extraction,
pattern recognition, clustering, and dimensionality reduction. Supervising learning
methods train the neural data using underlying functions to map to a given output and
automatically discover the relationships between input data and output labels. The
most common applications of supervised learning are classification and regression.
Introductory Chapter: Methods and Applications of Neural Signal Processing
DOI: http://dx.doi.org/10.5772/intechopen.93335
3.2.4 Time-frequency analysis
By combining the advantages of temporal and frequency analyses, the researchers realized the power of time-frequency analysis. As an example, using decomposition techniques, a signal can be decomposed into intrinsic mode functions (IMF)
and instantaneous frequencies over time can be obtained by applying methods such
as Hilbert spectral analysis. The most significant advantage of this technique is that
the nonlinear, nonstationary recorded neural signals can be transformed into linear
and stationary components. These components are usually physically meaningful since the special features are localized in their instantaneous frequencies and
represent meaningful behavioral information in the time-frequency domain. Timefrequency analysis is extensively implemented in neural signal processing since the
individual analysis in the time or frequency domain comes with respective disadvantages, and time-frequency analysis trades off time and frequency resolution to
get the best representation of the signals. Other techniques such as spectrogram and
STFT are most performed by segmenting a signal into short periods and estimating
the spectrum over sliding windows.
3.3 Dimensionality reduction
A critical procedure in neural signal processing is to reduce the high dimensionality of the recorded neural data. These data could be brain images, multi-electrode
signals, network potentials, or high-dimensional neural features. Several algorithms
can be applied linearly or nonlinearly to preserve the most useful components and
remove redundancies. Principal component analysis (PCA) is to find the direction of maximum variance and thereby build the principal components (weighted
linear combinations) based on the observed variance. Linear discriminant analysis
(LDA) performs similar to PCA but tends to minimize the variance within a group
of neural data and maximize the distance between groups of neural data. Thus,
PCA is described as an unsupervised algorithm implemented for feature extraction, and LDA is described as a supervised algorithm that uses training based on
labels for groups of data. Other methods that are used most often are CCA and
ICA. Canonical correlation analysis (CCA) is yet another method for exploring the
relationships between two multivariate sets of variables allowing us to summarize
the relationships into a lesser number of variables while preserving the essential
features of the relationships. Independent component analysis (ICA) is a blind
source separation method rather than a dimensionality reduction method. Neural
signals consist of recordings of potentials that are presumably generated by mixing
some underlying components of brain activity. ICA can theoretically isolate these
underlying components of brain activity by computing independent components.
Additionally, ICA can also be used as a filtering method to remove signal artifacts
generated by eye blinks or other artifacts in EEG signals.
3.4 Machine learning algorithms
Machine learning or deep learning algorithms have become increasingly popular
and are being implemented in many fields. They can be broadly divided into unsupervised learning and supervised learning. Unsupervised learning methods aim to
extract hidden structures within the neural data, commonly used for feature extraction,
pattern recognition, clustering, and dimensionality reduction. Supervising learning
methods train the neural data using underlying functions to map to a given output and
automatically discover the relationships between input data and output labels. The
most common applications of supervised learning are classification and regression.