Exploring the Patterns in Brain Signals: State-of-the-Art of Machine Learning

It is certainly true that brain signals are extremely difficult to analyze because these types of complex data contain a lot of noise, and extracting meaningful insights are considerably challenging. But with the advent of state-of-the-art machine learning (ML) technology, it has become much easier and more accurate to inspect. In this article, I will discuss how brain signals can be analyzed using state-of-the-art ML.

What will you learn after reading this article?

Brain-Computer Interface (BCI)

Application of BCI in the real world

What do you need to know to work on brain signal analysis in terms of ML?

How to use machine learning for pattern recognition of brain signals?

Let’s begin………………

Brain-Computer Interface (BCI)

Do you know that Neuroscience is the scientific study of the nervous system? With the discovery of BCI, it has become possible to find many insights into this field because it is directly interconnected between the human brain and the computer system. Electrical impulses generated by the brain are used in the technique. These impulses are amplified by a device and sent to software that calculates how to manage their computer. Now the question is, how does the brain produce an electrical signal since the human brain is formed of non-conducting tissues?

As you know that our human brain consists of 1000 billion microscopic cells, often called “Neuron”, so generally speaking, it is quite impossible to count the neurons!! It can be said that the “Action Potential” is used by neurons to transmit electrical impulses. The influx of positively charged ions across the neural membrane causes this event. Neurons, like all cells, maintain varying ion concentrations throughout their cell membranes.

Application of BCI in the real world

Several advantages can be found in terms of the BCI, but the major applications have been listed below:

• Individuals who have lost control of their muscles or are paralyzed
• People with poor neuromuscular control who experience muscle twitches and restricted eye movement.
• Individuals with good neuromuscular control require muscle-related conducive technology to complete certain tasks.

What do you need to know to work on brain signal analysis in terms of ML?

Even though the BCI has the potential impact in the real world, why would you require the ML approach? Because evaluating and interpreting data collected from these BCI device is a difficult task, software tools are being created to assist researchers. In other words, ML can analyze many types of complex data that is not possible with conventional tools; furthermore, With the use of ML, data analysis of important sectors from healthcare is becoming very easy nowadays. More importantly, there is no complete syllabus from which you can obtain some basic ideas in terms of Brain Signal analysis using ML; however, some major key points have been highlighted below for consideration:

• Python programming
• Ability to make decisions from data
• Clear understanding of Brain-Computer Interface (BCI) and Electroencephalography (EEG) data.
• Neural Network

How to use machine learning for pattern recognition of brain signals?

Let’s talk about Machine Learning (ML), one of the hot topics of this century😏 Brain signal analysis can be carried out by following the four significant steps: Collecting the brain signals data, Data cleaning, Features Extraction, Features Selection, Stage of the Classification and Brain Network Visualization. Fig.1 illustrates the major steps of brain signal classification and analysis.

Fig.1: Steps of the Brain Pattern Recognition

Phase 01: Collecting brain signal data:

Data collection is important for machine learning-based work while the decision is made by observing the data, so, before jumping to work, it is necessary to collect the information needed to perform the brain signal analysis. The following link can be utilized for collecting the Electroencephalogram (EEG) dataset. The most common use of EEG is to diagnose epilepsy, which causes abnormal EEG results. It can also be used to detect sleep problems, anesthetic depth, coma, encephalopathies, and brain death. https://sccn.ucsd.edu/~arno/fam2data/publicly_available_EEG_data.html

Phase 02: Data Preprocessing

Using techniques such as band-pass filtering and Bayesian denoising, we must eliminate noise and extra characteristics from EEG recordings at this stage. This stage will prepare the data for processing, making it easier to detect seizure-related signals and removing any undesirable artifacts that could skew the final result.

Phase 03: EEG Signals Illustration

It is vital to decide in which representation domain signals are evaluated in order to ensure that the best EEG signal representation is employed in seizure diagnostics. Time, frequency, and joint time-frequency domains are the most common representation domains employed by researchers.

Phase 04: EEG Features Extraction

Since the computer only understands binary data, before applying any machine learning algorithm, we need to convert our dataset into a machine readable language so that the computer can recognize the data accurately. For the case of Brain Signal processing, it is extremely significant extracting the features from the EEG data. Several approaches can be found in terms of features extraction, but the following techniques are being used frequently:

(a) IF Measure, (b) Image descriptors and image processing techniques
such as shape and texture descriptor and local binary pattern (LBP) descriptor, (c)Entropy features, (d) Texture features

Phase 05: Features Selection

There will be redundant or unnecessary features in the extraction, therefore not all of the features are decisive. To ensure the quality and correctness of the final brain signal analysis and classification, this stage involves filtering out the most irrelevant features and eliminating the undesired ones.

Phase 06: Stage of the Classification

The previously extracted and filtered characteristics are now ready to be used in the final diagnostics and classification. Several classifiers, such as an artificial neural network and a support vector machine, as well as a decision tree, can be used for classification at this step. The performance of the classifier can be evaluated using a cross-validation method. Validation can be done using the leave-one-out strategy, which yields a nearly unbiased approximation of the true generalization error.

Phase 07: Brain Network Visualization

The coordinates of the associated channels can be assigned to vertices to depict brain functional networks. Additionally, network visualization techniques such as the stress majorization methodology and the binary stress model can be used to display the network topology.

In conclusion, although recent technologies such as BCI are working quite well, it is quite complex to analyze BCI data in this regard, so the advent of machine learning can play an important role. This article explains the common pipelines for brain signal analysis and classification that may be helpful for those who are thinking of starting work in this field.

References:

1. L. Boubchir, S. Al-Maadeed, A. Bouridane and A. A. Chérif. “Time-frequency Image Descriptors-based Features for EEG Epileptic Seizure Activities Detection and Classification.” In: 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 867–871, 2015.
2. L. Boubchir, S. Al-Maadeed, A. Bouridane, and A. A. Chérif. “Classification of EEG signals for detection of epileptic seizure activities based on LBP descriptor of time-frequency images.” In: 2015 IEEE International Conference on Image Processing (ICIP), 2015, pp. 3758–3762