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Das, R.K.; Martin, A.; Zurales, T.; Dowling, D.; Khan, A. Electroencephalography Data Analysis Software. Encyclopedia. Available online: https://encyclopedia.pub/entry/46001 (accessed on 25 June 2024).
Das RK, Martin A, Zurales T, Dowling D, Khan A. Electroencephalography Data Analysis Software. Encyclopedia. Available at: https://encyclopedia.pub/entry/46001. Accessed June 25, 2024.
Das, Rupak Kumar, Anna Martin, Tom Zurales, Dale Dowling, Arshia Khan. "Electroencephalography Data Analysis Software" Encyclopedia, https://encyclopedia.pub/entry/46001 (accessed June 25, 2024).
Das, R.K., Martin, A., Zurales, T., Dowling, D., & Khan, A. (2023, June 25). Electroencephalography Data Analysis Software. In Encyclopedia. https://encyclopedia.pub/entry/46001
Das, Rupak Kumar, et al. "Electroencephalography Data Analysis Software." Encyclopedia. Web. 25 June, 2023.
Electroencephalography Data Analysis Software
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Electroencephalography (EEG) is a mechanism to understand the brain’s functioning by analyzing brain electrical signals. More recently, it has been more commonly used in studies that are focused on the causation and effect of dementia. More tools are now available to gather EEG data. This brings about the challenge of understanding brain signals, which involves signal processing. Professionals with an electrical engineering background are very comfortable analyzing EEG data. Still, scientists in computer science and related fields need a source that can identify all the tools available and the process of analyzing the data.

artifact removal EEG EEG tools

1. Introduction

Electroencephalography (EEG) is a method of measuring the state of mind, as well as the output of the brain in response to a given stimulus, either internal or external. This is typically done by attaching electrodes to the scalp, allowing the stronger electrical signals produced within the brain to be measured as a change in voltage across the electrodes. Because the signals collected this way are very weak, they are very sensitive to noise from the environment and other electrical signals produced by the individual being measured, such as those produced by muscle movements and even the movements of fluid in the head. This sensitivity facilitates the need for effective cleaning methods for the recorded signal, including filtration and algorithmic removal of known noise, also called artifacts. Beyond cleaning the signal, numerous methods of analysis are often required to understand the meaning of the collected signals and their significance. This includes traditional as well as algorithmic analysis of the data.
EEG signals can be used in biomedical research to examine the dynamics of brain activity during different cognitive and behavioral tasks [1], emotion recognition [2] neuromarketing research [3]. The effects of various treatments and interventions on brain function, such as medication, psychotherapy [4], or brain stimulation methods [5], can also be studied using EEG. These signals are used clinically to diagnose and track a variety of neurological disorders [6] in addition to being used for research. EEG can be used, for instance, to diagnose and detect seizures [7], to locate epileptic foci [8] prior to surgical intervention, and to track the development of degenerative brain conditions like Alzheimer’s [9].

2. EEG Signal Processing Methods

The first step to analyzing newly collected EEG data is preprocessing. Preprocessing involves passing the data through filters that highlight the data of interest. These filtering methods can not only remove data irrelevant to the study (as in the case of high and low pass filters), but they can also amplify data of interest by making it more visible in the data collection. The removal of general signal noise (background noise) and artifacts linked to eye movement, head movement, jaw movement, other muscle movements, blinking, and head tilting are all possible using EEG filtration methods. Filtration techniques may also eliminate signal frequencies that can be ignored or dismissed. The following are some fundamental filtration processes.

2.1. Bessel Filter

A Bessel filter (also known as a Thomson filter) is an analog linear filter with a maximally linear phase response. The transition from passband to stopband is slower with a Bessel filter than with other filters of the same order. It has a small amount of overshoot compared to the most common frequency domain filters.

2.2. Band Pass Filter

The bandpass filter only allows signals within a specific frequency range to pass. A mixture of high and low-frequency filtering methods can be used to achieve this feature. A bandpass filter can be used to isolate a frequency range by removing frequencies higher than the highest predicted frequency and lower than the lowest expected frequency when the researcher knows the precise frequency range for that study.

2.3. Butterworth Filter

A Butterworth filter is a signal processing filter whose frequency response in the passband is as flat as possible. That is why it is also known as the “maximally flat magnitude filter.” The order of the filter is determined by the number of reactive elements (capacitors) used in the circuit. It is used as an anti-aliasing filter in data converter applications because of its maximally flat frequency response in the passband. It is also used in audio processing applications, various communication and control systems, motion analysis, etc. To remove eye-blink artifacts, the Butterworth bandpass filter is used to remove all frequencies below 20 Hz in this study [10].

2.4. Chebyshev Filter

Chebyshev filters are commonly used to separate frequency bands from one another. It is an analog and digital filter having passband ripple or stopband ripple along with a steeper roll-off than Butterworth filters. Chebyshev and Inverse Chebyshev filters are used to describe type-1 and type-2 filters, respectively. The Chebyshev filter is used in the IIR filter to eliminate any artifacts from the EEG signal to achieve maximum spectral efficiency [11].

2.5. Finite Impulse Response (FIR) Filter

FIR filters are a type of filter that produces a filtered output by combining prior and current signal data. For EEG analysis, FIR filters are preferable over IIR filters because they do not distort waveforms. The FIR filter is used in [12] for detecting the EEG waves.

2.6. High/Low Pass Filter

The frequency range of EEG data is between 0.1 and 50 Hz. However, as previously mentioned, different noise sources might emerge in EEG data. Movement, breathing, and external radio waves can all cause this. A high-pass filter can filter out slow frequencies of less than 0.1 Hz or higher frequencies greater than 50 Hz. As a result, the user has a clear signal to analyze.

2.7. Infinite Impulse Response (IIR) Filter

The IIR filter creates an impulse response in the same way that an FIR filter does, but over an infinite length of time rather than a finite period.

2.8. Least Square Filter

Least Mean Square (LMS) filters are adaptive filters that can “learn” a new unknown transfer function. The filter coefficients of LMS filters are changed depending on the instantaneous error signal using a gradient descent algorithm.

2.9. K-Nearest Neighbors

The k-nearest neighbors’ technique (k-NN) is a well-known non-parametric signal processing method. KNN can be used to perform both classification and regression forecasting problems. It is widely utilized due to its simplicity of interpretation and quick calculation time. Emotion recognition using EEG signals is nowadays a famous study. The KNN classifier is used here [13] for the classification technique for recognizing emotions from multichannel EEG signals.

2.10. Naive Bayes

The Naive Bayes classifier is a classification technique based on Bayes’ Theorem. It is a probabilistic classifier that incorporates strong independence assumptions. Naive Bayes classifiers have excelled in various challenging real-world circumstances despite their naïve design and simplistic assumptions. The advantage of naive Bayes is that it can estimate classification parameters with a small amount of training data. This classification technique can be used to classify mental stress using EEG signals [14].

2.11. Notch Filter

It can be advantageous to remove signals within a specified frequency range while studying a signal with a known frequency. A notch filter (also known as a band stop filter) is a filter that “stops” a specific frequency range (not passed through). On the other hand, high or low-frequency filtering rejects all frequencies of a signal above or below a set threshold value. The notch filter functions as both a high-pass and a low-pass filter, preventing signals that would be blocked by both while passing signals that would be filtered by only one. This filter can be used to remove power line interference (50 Hz) from EEG signals [15].

2.12. Non-Local Means (NLM) Filter

Non-local means (NLM) is a widespread technique generally used for image denoising. However, the NLM filter with the combination of wavelet transform is also used to remove artifacts from EMG and EEG signals [16].

2.13. Partial Least Squares

PLS regression reduces predictors to a smaller collection of uncorrelated components. It does least squares regression on these components rather than the original data. When the predictors are collinear, or the system has more predictors than observations, regular least-squares regression either yields coefficients with high standard errors or fails; PLS regression is incredibly effective.

2.14. Random Forest Classifier

A random forest, or a random decision forest, is a classification and regression technique based on decision trees. During training time, many decision trees are constructed in this algorithm. Most trees select the output class of this algorithm. Those trees are relatively uncorrelated, which is the main reason for their excellent accuracy. A random forest classifier can be used to analyze human mental states [17] and perform automatic emotion recognition [18].

2.15. Regularized Discriminant Analysis

Regularized discriminant analysis (RDA) is the general form of linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA). This analysis is used for both classification and dimensionality reduction. This method is used to figure out which variables distinguish two or more naturally occurring groups. This is very similar to the analysis of variance (ANOVA).

References

  1. Pavlov, A.N.; Pitsik, E.N.; Frolov, N.S.; Badarin, A.; Pavlova, O.N.; Hramov, A.E. Age-related distinctions in eeg signals during execution of motor tasks characterized in terms of long-range correlations. Sensors 2020, 20, 5843.
  2. Yang, H.; Han, J.; Min, K. A multi-column CNN model for emotion recognition from EEG signals. Sensors 2019, 19, 4736.
  3. Aldayel, M.; Ykhlef, M.; Al-Nafjan, A. Deep learning for EEG-based preference classification in neuromarketing. Appl. Sci. 2020, 10, 1525.
  4. Kopańska, M.; Ochojska, D.; Dejnowicz-Velitchkov, A.; Banaś-Ząbczyk, A. Quantitative Electroencephalography (QEEG) as an Innovative Diagnostic Tool in Mental Disorders. Int. J. Environ. Res. Public Health 2022, 19, 2465.
  5. Annarumma, L.; D’Atri, A.; Alfonsi, V.; De Gennaro, L. The efficacy of transcranial current stimulation techniques to modulate resting-state EEG, to affect vigilance and to promote sleepiness. Brain Sci. 2018, 8, 137.
  6. Alturki, F.A.; AlSharabi, K.; Abdurraqeeb, A.M.; Aljalal, M. EEG signal analysis for diagnosing neurological disorders using discrete wavelet transform and intelligent techniques. Sensors 2020, 20, 2505.
  7. Prasanna, J.; Subathra MS, P.; Mohammed, M.A.; Damaševičius, R.; Sairamya, N.J.; George, S.T. Automated epileptic seizure detection in pediatric subjects of CHB-MIT EEG database—A survey. J. Pers. Med. 2021, 11, 1028.
  8. Ebrahimzadeh, E.; Shams, M.; Rahimpour Jounghani, A.; Fayaz, F.; Mirbagheri, M.; Hakimi, N.; Rajabion, L.; Soltanian-Zadeh, H. Localizing confined epileptic foci in patients with an unclear focus or presumed multifocality using a component-based EEG-fMRI method. Cogn. Neurodyn. 2021, 15, 207–222.
  9. Avila, J.; Perry, G. A multilevel view of the development of Alzheimer’s disease. Neuroscience 2021, 457, 283–293.
  10. Kasim, Ö.; Tosun, M. Effective removal of eye-blink artifacts in EEG signals with semantic segmentation. Signal Image Video Process. 2022, 16, 1289–1295.
  11. Lasefr, Z.; Ayyalasomayajula, S.S.V.; Elleithy, K. Epilepsy seizure detection using EEG signals. In Proceedings of the 2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON), New York, NY, USA, 19–21 October 2017; IEEE: New York, NY, USA, 2017; pp. 162–167.
  12. Melinda, M.; Syahrial; Yunidar; Bahri, A.; Irhamsyah, M. Finite Impulse Response Filter for Electroencephalogram Waves Detection. Green Intell. Syst. Appl. 2022, 2, 7–19.
  13. Li, M.; Xu, H.; Liu, X.; Lu, S. Emotion recognition from multichannel EEG signals using K-nearest neighbor classification. Technol. Health Care 2018, 26, 509–519.
  14. Siuly; Wang, H.; Zhang, Y. Detection of motor imagery EEG signals employing Naïve Bayes based learning process. Measurement 2016, 86, 148–158.
  15. Tibdewal, M.N.; Mahadevappa, M.; Ray, A.K.; Malokar, M.; Dey, H.R. Power line and ocular artifact denoising from EEG using notch filter and wavelet transform. In Proceedings of the 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom), New Delhi, India, 16–18 March 2016; IEEE: New York, NY, USA, 2016; pp. 1654–1659.
  16. Phadikar, S.; Sinha, N.; Ghosh, R.; Ghaderpour, E. Automatic Muscle Artifacts Identification and Removal from Single-Channel EEG Using Wavelet Transform with Meta-Heuristically Optimized Non-Local Means Filter. Sensors 2022, 22, 2948.
  17. Edla, D.R.; Mangalorekar, K.; Dhavalikar, G.; Dodia, S. Classification of EEG data for human mental state analysis using Random Forest Classifier. Procedia Comput. Sci. 2018, 132, 1523–1532.
  18. Veeramallu, G.K.P.; Anupalli, Y.; Kumar Jilumudi, S.; Bhattacharyya, A. EEG based automatic emotion recognition using EMD and random forest classifier. In Proceedings of the 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Kanpur, India, 6–8 July 2019; IEEE: New York, NY, USA, 2019; pp. 1–6.
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