EEG analysis

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EEG analysis is exploiting mathematical signal analysis methods and computer technology to extract information from electroencephalography (EEG) signals. The targets of EEG analysis are to help researchers gain a better understanding of the brain; assist physicians in diagnosis and treatment choices; and to boost brain-computer interface (BCI) technology. There are many ways to roughly categorize EEG analysis methods. If a mathematical model is exploited to fit the sampled EEG signals, [1] the method can be categorized as parametric, otherwise, it is a non-parametric method. Traditionally, most EEG analysis methods fall into four categories: time domain, frequency domain, time-frequency domain, and nonlinear methods. [2] There are also later methods including deep neural networks (DNNs).

Contents

Although it is extremely important for researchers to choose the appropriate EEG analysis methods according to their research objectives and the results they want to obtain, the finalized studies provide reference for future research, help solve existing problems and prepare the ground for future studies.  

Methods

Frequency domain methods

Frequency domain analysis, also known as spectral analysis, is the most conventional yet one of the most powerful and standard methods for EEG analysis. It gives insight into information contained in the frequency domain of EEG waveforms by adopting statistical and Fourier Transform methods. [3] Among all the spectral methods, power spectral analysis is the most commonly used, since the power spectrum reflects the 'frequency content' of the signal or the distribution of signal power over frequency. [4] This technique can be used to investigate the energy changes of different frequency components in EEG signals during EEG analysis. It is appropriate for use in the study of neurological diseases and brain science, as these conditions can cause EEG energy changes during changes in state, such as changes in sleep phase, seizures, and emotional states. [5]

Time domain methods

There are two important methods for time domain EEG analysis: Linear Prediction and Component Analysis. Generally, Linear Prediction gives the estimated value equal to a linear combination of the past output value with the present and past input value. And Component Analysis is an unsupervised method in which the data set is mapped to a feature set. [6] Notably, the parameters in time domain methods are entirely based on time, but they can also be extracted from statistical moments of the power spectrum. As a result, time domain method builds a bridge between physical time interpretation and conventional spectral analysis. [7] Besides, time domain methods offer a way to on-line measurement of basic signal properties by means of a time-based calculation, which requires less complex equipment compared to conventional frequency analysis. [8]

Time-frequency domain methods

Time-frequency analysis is typically performed using the Wavelet Transform (WT), Empirical Mode Decomposition (EMD), Wigner-Ville Distribution (WVD), and Short-time Fourier Transform (STFT). [9]

WT, a typical time-frequency domain method, can extract and represent properties from transient biological signals. Specifically, through wavelet decomposition of the EEG records, transient features can be accurately captured and localized in both time and frequency context. [10] Thus WT is like a mathematical microscope that can analyze different scales of neural rhythms and investigate small-scale oscillations of the brain signals while ignoring the contribution of other scales. [11] [12] Apart from WT, there is another prominent time-frequency method called Hilbert-Huang Transform, which can decompose EEG signals into a set of oscillatory components called Intrinsic Mode Function (IMF) in order to capture instantaneous frequency data. [13] [14]

Nonlinear methods

Many phenomena in nature are nonlinear and non-stationary, and so are EEG signals. This attribute adds more complexity to the interpretation of EEG signals, rendering linear methods (methods mentioned above) limited. Since 1985 when two pioneers in nonlinear EEG analysis, Rapp and Bobloyantz, published their first results, the theory of nonlinear dynamic systems, also called 'chaos theory', has been broadly applied to the field of EEG analysis. [15] To conduct nonlinear EEG analysis, researchers have adopted many useful nonlinear parameters such as Lyapunov Exponent, Correlation Dimension, and entropies like Approximate Entropy and Sample Entropy. [16] [17]

ANN methods

The implementation of Artificial Neural Networks (ANN) is presented for classification of electroencephalogram (EEG) signals. In most cases, EEG data involves a preprocess of wavelet transform before putting into the neural networks. [18] [19] RNN (recurrent neural networks) was once considerably applied in studies of ANN implementations in EEG analysis. [20] [21] Until the boom of deep learning and CNN (Convolutional Neural Networks), CNN method becomes a new favorite in recent studies of EEG analysis employing deep learning. With cropped training for the deep CNN to reach competitive accuracies on the dataset, deep CNN has presented a superior decoding performance. [22] Moreover, the big EEG data, as the input of ANN, calls for the need for safe storage and high computational resources for real-time processing. To address these challenges, a cloud-based deep learning has been proposed and presented for real-time analysis of big EEG data. [23]

Applications

EEG, a non-invasive procedure, is used to record brain activity in cognitive studies, different clinical applications and brain-computer interfaces (BCI). EEG recording is both an easily portable method for different clinical uses and open to applications in various fields as it directly measures collective neural activity. [24]

In terms of cost, EEG recording is considered less expensive than other non-invasive brain signal recording technologies such as functional magnetic resonance imaging (fMRI), magnetoencephalography (MEG), and near-infrared spectroscopy (NIRS). [25]

Clinical

EEG analysis is widely used in brain-disease diagnosis and assessment. In the domain of epileptic seizures, the detection of epileptiform discharges in the EEG is an important component in the diagnosis of epilepsy. Careful analyses of the EEG records can provide valuable insight and improved understanding of the mechanisms causing epileptic disorders. [26] Besides, EEG analysis also helps much with the detection of Alzheimer's disease, [27] tremor, etc.

BCI (Brain-computer Interface)

EEG recordings during right and left motor imagery allow one to establish a new communication channel. [28] Based on real-time EEG analysis with subject-specific spatial patterns, a brain–computer interface (BCI) can be used to develop a simple binary response for the control of a device.

EEG-based BCI approaches, together with advances in machine learning and other technologies such as wireless recording, aim to contribute to the daily lives of people with disabilities and significantly improve their quality of life. [29] Such an EEG-based BCI can help, e.g., patients with amyotrophic lateral sclerosis, with some daily activities.

Analysis tool

Brainstorm is a collaborative, open-source application dedicated to the analysis of brain recordings including MEG, EEG, fNIRS, ECoG, depth electrodes and animal invasive neurophysiology. [30] The objective of Brainstorm is to share a comprehensive set of user-friendly tools with the scientific community using MEG/EEG as an experimental technique. Brainstorm offers rich and intuitive graphic interface for physicians and researchers, which does not require any programming knowledge. Some other relative open source analysis software include FieldTrip, etc.

Others

Combined with facial expressions analysis, EEG analysis offers the function of continuous emotion detection, which can be used to find the emotional traces of videos. [31] Some other applications include EEG-based brain mapping, personalized EEG-based encryptor, EEG-Based image annotation system, etc.

See also

Related Research Articles

Digital signal processing (DSP) is the use of digital processing, such as by computers or more specialized digital signal processors, to perform a wide variety of signal processing operations. The digital signals processed in this manner are a sequence of numbers that represent samples of a continuous variable in a domain such as time, space, or frequency. In digital electronics, a digital signal is represented as a pulse train, which is typically generated by the switching of a transistor.

<span class="mw-page-title-main">Wavelet</span> Function for integral Fourier-like transform

A wavelet is a wave-like oscillation with an amplitude that begins at zero, increases or decreases, and then returns to zero one or more times. Wavelets are termed a "brief oscillation". A taxonomy of wavelets has been established, based on the number and direction of its pulses. Wavelets are imbued with specific properties that make them useful for signal processing.

<span class="mw-page-title-main">Magnetoencephalography</span> Mapping brain activity by recording magnetic fields produced by currents in the brain

Magnetoencephalography (MEG) is a functional neuroimaging technique for mapping brain activity by recording magnetic fields produced by electrical currents occurring naturally in the brain, using very sensitive magnetometers. Arrays of SQUIDs are currently the most common magnetometer, while the SERF magnetometer is being investigated for future machines. Applications of MEG include basic research into perceptual and cognitive brain processes, localizing regions affected by pathology before surgical removal, determining the function of various parts of the brain, and neurofeedback. This can be applied in a clinical setting to find locations of abnormalities as well as in an experimental setting to simply measure brain activity.

<span class="mw-page-title-main">Morlet wavelet</span>

In mathematics, the Morlet wavelet is a wavelet composed of a complex exponential (carrier) multiplied by a Gaussian window (envelope). This wavelet is closely related to human perception, both hearing and vision.

<span class="mw-page-title-main">Neurofeedback</span> Type of biofeedback

Neurofeedback is a form of biofeedback that uses electrical potentials in the brain to reinforce desired brain states through operant conditioning. This process is non-invasive and typically collects brain activity data using electroencephalography (EEG). Several neurofeedback protocols exist, with potential additional benefit from use of quantitative electroencephalography (QEEG) or functional magnetic resonance imaging (fMRI) to localize and personalize treatment. Related technologies include functional near-infrared spectroscopy-mediated (fNIRS) neurofeedback, hemoencephalography biofeedback (HEG), and fMRI biofeedback.

<span class="mw-page-title-main">Brain–computer interface</span> Direct communication pathway between an enhanced or wired brain and an external device

A brain–computer interface (BCI), sometimes called a brain–machine interface (BMI) or smartbrain, is a direct communication pathway between the brain's electrical activity and an external device, most commonly a computer or robotic limb. BCIs are often directed at researching, mapping, assisting, augmenting, or repairing human cognitive or sensory-motor functions. They are often conceptualized as a human–machine interface that skips the intermediary component of the physical movement of body parts, although they also raise the possibility of the erasure of the discreteness of brain and machine. Implementations of BCIs range from non-invasive and partially invasive to invasive, based on how close electrodes get to brain tissue.

<span class="mw-page-title-main">Continuous wavelet transform</span> Integral transform

In mathematics, the continuous wavelet transform (CWT) is a formal tool that provides an overcomplete representation of a signal by letting the translation and scale parameter of the wavelets vary continuously.

A gamma wave or gamma rhythm is a pattern of neural oscillation in humans with a frequency between 25 and 140 Hz, the 40 Hz point being of particular interest. Gamma rhythms are correlated with large-scale brain network activity and cognitive phenomena such as working memory, attention, and perceptual grouping, and can be increased in amplitude via meditation or neurostimulation. Altered gamma activity has been observed in many mood and cognitive disorders such as Alzheimer's disease, epilepsy, and schizophrenia.

<span class="mw-page-title-main">Neural oscillation</span> Brainwaves, repetitive patterns of neural activity in the central nervous system

Neural oscillations, or brainwaves, are rhythmic or repetitive patterns of neural activity in the central nervous system. Neural tissue can generate oscillatory activity in many ways, driven either by mechanisms within individual neurons or by interactions between neurons. In individual neurons, oscillations can appear either as oscillations in membrane potential or as rhythmic patterns of action potentials, which then produce oscillatory activation of post-synaptic neurons. At the level of neural ensembles, synchronized activity of large numbers of neurons can give rise to macroscopic oscillations, which can be observed in an electroencephalogram. Oscillatory activity in groups of neurons generally arises from feedback connections between the neurons that result in the synchronization of their firing patterns. The interaction between neurons can give rise to oscillations at a different frequency than the firing frequency of individual neurons. A well-known example of macroscopic neural oscillations is alpha activity.

<span class="mw-page-title-main">Electrocorticography</span> Type of electrophysiological monitoring

Electrocorticography (ECoG), a type of intracranial electroencephalography (iEEG), is a type of electrophysiological monitoring that uses electrodes placed directly on the exposed surface of the brain to record electrical activity from the cerebral cortex. In contrast, conventional electroencephalography (EEG) electrodes monitor this activity from outside the skull. ECoG may be performed either in the operating room during surgery or outside of surgery. Because a craniotomy is required to implant the electrode grid, ECoG is an invasive procedure.

<span class="mw-page-title-main">Mu wave</span> Electrical activity in the part of the brain controlling voluntary movement

The sensorimotor mu rhythm, also known as mu wave, comb or wicket rhythms or arciform rhythms, are synchronized patterns of electrical activity involving large numbers of neurons, probably of the pyramidal type, in the part of the brain that controls voluntary movement. These patterns as measured by electroencephalography (EEG), magnetoencephalography (MEG), or electrocorticography (ECoG), repeat at a frequency of 7.5–12.5 Hz, and are most prominent when the body is physically at rest. Unlike the alpha wave, which occurs at a similar frequency over the resting visual cortex at the back of the scalp, the mu rhythm is found over the motor cortex, in a band approximately from ear to ear. People suppress mu rhythms when they perform motor actions or, with practice, when they visualize performing motor actions. This suppression is called desynchronization of the wave because EEG wave forms are caused by large numbers of neurons firing in synchrony. The mu rhythm is even suppressed when one observes another person performing a motor action or an abstract motion with biological characteristics. Researchers such as V. S. Ramachandran and colleagues have suggested that this is a sign that the mirror neuron system is involved in mu rhythm suppression, although others disagree.

Synaptic noise refers to the constant bombardment of synaptic activity in neurons. This occurs in the background of a cell when potentials are produced without the nerve stimulation of an action potential, and are due to the inherently random nature of synapses. These random potentials have similar time courses as excitatory postsynaptic potentials (EPSPs) and inhibitory postsynaptic potentials (IPSPs), yet they lead to variable neuronal responses. The variability is due to differences in the discharge times of action potentials.

Fault detection, isolation, and recovery (FDIR) is a subfield of control engineering which concerns itself with monitoring a system, identifying when a fault has occurred, and pinpointing the type of fault and its location. Two approaches can be distinguished: A direct pattern recognition of sensor readings that indicate a fault and an analysis of the discrepancy between the sensor readings and expected values, derived from some model. In the latter case, it is typical that a fault is said to be detected if the discrepancy or residual goes above a certain threshold. It is then the task of fault isolation to categorize the type of fault and its location in the machinery. Fault detection and isolation (FDI) techniques can be broadly classified into two categories. These include model-based FDI and signal processing based FDI.

<span class="mw-page-title-main">Electroencephalography</span> Electrophysiological monitoring method to record electrical activity of the brain

Electroencephalography (EEG) is a method to record an electrogram of the spontaneous electrical activity of the brain. The biosignals detected by EEG have been shown to represent the postsynaptic potentials of pyramidal neurons in the neocortex and allocortex. It is typically non-invasive, with the EEG electrodes placed along the scalp using the International 10–20 system, or variations of it. Electrocorticography, involving surgical placement of electrodes, is sometimes called "intracranial EEG". Clinical interpretation of EEG recordings is most often performed by visual inspection of the tracing or quantitative EEG analysis.

Quantitative electroencephalography is a field concerned with the numerical analysis of electroencephalography (EEG) data and associated behavioral correlates.

Brain connectivity estimators represent patterns of links in the brain. Connectivity can be considered at different levels of the brain's organisation: from neurons, to neural assemblies and brain structures. Brain connectivity involves different concepts such as: neuroanatomical or structural connectivity, functional connectivity and effective connectivity.

<span class="mw-page-title-main">Burst suppression</span>

Burst suppression is an electroencephalography (EEG) pattern that is characterized by periods of high-voltage electrical activity alternating with periods of no activity in the brain. The pattern is found in patients with inactivated brain states, such as from general anesthesia, coma, or hypothermia. This pattern can be physiological, as during early development, or pathological, as in diseases such as Ohtahara syndrome.

Corticocortical coherence is referred to the synchrony in the neural activity of different cortical brain areas. The neural activities are picked up by electrophysiological recordings from the brain. It is a method to study the brain's neural communication and function at rest or during functional tasks.

Neural dust is a hypothetical class of nanometer-sized devices operated as wirelessly powered nerve sensors; it is a type of brain–computer interface. The sensors may be used to study, monitor, or control the nerves and muscles and to remotely monitor neural activity. In practice, a medical treatment could introduce thousands of neural dust devices into human brains. The term is derived from "smart dust", as the sensors used as neural dust may also be defined by this concept.

<span class="mw-page-title-main">Dimitri Van De Ville</span> Swiss-Belgian computer scientist and neuroscientist specialized in brain activity networks

Dimitri Van De Ville is a Swiss and Belgian computer scientist and neuroscientist specialized in dynamical and network aspects of brain activity. He is a professor of bioengineering at EPFL and the head of the Medical Image Processing Laboratory at EPFL's School of Engineering.

References

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[1] [2]

  1. ↑ Stress Analysis using Feature Extraction Approach Using EEG Signal Bamanikar, A.A. , Patil, R.V. , Patil, L.V. International Journal of Intelligent Systems and Applications in EngineeringThis link is disabled., 2024, 12(5s), pp. 409–417
  2. ↑ Stress & Emotion Recognition Using Sentiment Analysis with Brain Signal Bamanikar, A.A. , Patil, R.V. , Patil, L.V. 2022 IEEE 2nd International Conference on Mobile Networks and Wireless Communications, ICMNWC 2022, 2022
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