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Pattern recognition is becoming increasingly important topic in all sectors of society. From the optimization of processes in the industry to the detection and diagnosis of diseases in medicine. Brain-computer interfaces are introduced in this chapter. Systems capable of analyzing brain signal patterns, processing and interpreting them through machine and deep learning algorithms. In this chapter, a hybrid deep/machine learning ensemble system for brain pattern recognition is proposed. It is capable to recognize patterns and translate the decisions to BCI systems. For this, a public database (Physionet) with data of motor tasks and mental tasks is used. The development of this chapter consists of a brief summary of the state of the art, the presentation of the model together with some results and some promising conclusions.
University of Las Palmas de Gran Canaria (ULPGC), Las Palmas de Gran Canaria, Spain
Carlos M. Travieso-Gonzalez
University of Las Palmas de Gran Canaria (ULPGC), Las Palmas de Gran Canaria, Spain
*Address all correspondence to: nabil.ajali101@alu.ulpgc.es
1. Introduction
The brain is the most important organ in the human body. It processes, integrates and coordinates the information it receives from the organs and the senses and makes decisions, sending them to the rest of the body, like a processor in a computer. The brain works through electrochemical impulses, called synapses, which allow the transmission of information between neurons [1, 2, 3].
These impulses could be classified by their frequency into different types of brain waves. Delta (1–3 Hz), theta (3–8 Hz), alpha (8–13 Hz), beta (13–30 Hz), and gamma (30–100 Hz) waves [4]. These brain waves are the reflection of electrical activity (in microvolts) and therefore of thoughts and motor intentions. They can be captured by the electroencephalogram (EEG) and their study can lead to the detection of pathologies related to the brain (Alzheimer’s, Parkinson’s, epilepsy) [5, 6].
Figure 1 shows the EEG of a normal person, where 64 channels have been placed throughout the head (10–20 system) and brain waves are monitored over time. It can be seen how there are an infinity of patterns that provide important information about what is happening.
Figure 1.
Excerpt from a normal encephalogram. Channels on the left and brain waves monitored.
As a result of technological advances in both software and hardware, concepts such as artificial intelligence, machine learning or deep learning have been developed in last decades. This has allowed an evolution in many fields of society. In the field of brain signals, pattern recognition is of vital importance, both for diagnosis and for the development of applications that improve quality of life or simply for the development of mind-controlled tools or games. Thus, the Brain Computer Interfaces were born.
A brain-computer interface (BCI) or Brain-machine Interface is a system based on the recording or acquisition of the brain signal that is linked by direct communication (wired or wireless) to a machine or computer capable of interpreting and transforming thoughts or intentions into actual actions [7]. Figure 2 shows the complete process of a BCI. First the obtaining of brain signals and their processing. Subsequently, the machine associated with the system is capable of receiving and interpreting these signals and resulting in a response.
Figure 2.
Brain computer Interface workflow.
In this way, using a public database, called Physionet, which consists of 109 subjects who perform tests of motor intentions, we are working on a BCI system whose objective is to be able to recognize the patterns of these thought movements and transfer them to a robotic arm that moves accordingly. Contributing to the current state of the art a new method that seeks to take into account the immediate previous mental state (IPMS) of each subject to improve pattern recognition.
2. Pattern recognition with immediate previous mental state
2.1 State of art
From the last 10–15 years and until 2022, many studies have led to great advances in pattern recognition, human-computer interaction and in applications that use task classification or event-related potential (P300). Machine and deep learning algorithms and techniques have developed as computers and systems have improved over the years. They use different characteristics extracted from the data obtained from brain waves to classify or recognize certain patterns, getting better and better success in the pattern recognition and prediction [8, 9, 10, 11]. E.g., in 2018 by Bird et al. made predictions of mental states of relaxation, neutrality and concentration [12]. Also, in emotional mental states such as being negative, neutral and positive [13].
In the field of EEG, large amounts of studies have been carried out in those years, addressing issues such as EEG channel selection, methods for make an optimal feature extraction or types of classifiers to predict patterns. For example, Feng et al. [14] published in 2019 an optimized channel selection method based on multi-frequency CSP-Rank for BCI systems using motor images. Jiménez et al. [15], propose an upper limb to assist in the rehabilitation of people with cerebrovascular accident or people with some disability or amputation. There are even lines of research that began to focus on the creation of brain-machine systems capable of acting under the orders of thought.
In 2020, philanthropist Elon Musk and his company Neuralink created an implantable BCI system to control systems with the mind which was successfully implanted in a pig in 2021. In addition, he announced that a monkey had been successfully made to play video games using this device [16]. The downside is that these BCI devices are invasive and still in the early stages of development.
All these advances always depend on the nature of the problem. In many cases the EEG image is taken directly and by using Common Spatial Patterns (CSP) the problem is solved. Other times 3D or 2D matrices are created to evaluate the problem and applying deep learning methods such as the use of Convolutional Neural Networks (CNN) together with Long-Short Term Memory (LSTM) the problem is solved.
The advantage of these techniques is the automation and success of the learning process. But, on the other hand, the disadvantage is the cost in terms of computation and the large amount of data that is required.
Normally all systems have work using a common scheme, as shown in Figure 2, which has the following steps:
Signal acquisition. Directly/EEG database.
Feature extraction.
Method selection for pattern recognition.
Train-Validation-Test. Feedback.
Subsequently, the rank of success in brain task classification of many works and also the most important work to date is shown.
In this chapter, a large number of avant-garde articles have been reviewed to extract the most important concepts and ideas in this field in order to adequately explain and expose the work carried out.
2.2 Pattern recognition with immediate previous mental state
As stated above, current works use machine learning and deep learning to recognize brain patterns and classify them. The success rate vary depends on the database used, the signal processing, the feature extraction or the types of classifiers used. Many of these works are between 61 and 76% in their success rate [17, 18].
The best work to date is that provided by Zhang et al. [19] where they claim to achieve success rates in the classification of at least 93% and up to 98%, using the same database (Physionet). They do this through cascade deep learning techniques, mixing a 3-layer CNN with recurrent neural networks (RNN) to take into account spatio-temporal characteristics of the experiments.
In other hand, we have a previous work, that are pending to be published, where we achieved experiments with up to 93% success using a mixture of machine learning classifiers. Taking this into account, we have done a lecture of the state of the art and concluded that according to several studies, such as that of Roc et al. [9], the success rate of the classification of mental tasks by in BCI systems is directly related to the subject in question and also to his mental state at that moment (relaxed, altered, nervous).
2.2.1 Our proposal
For this reason, our proposal consists of following a generalized scheme where the signal from the database is acquired, processed, the features are extracted and then a classification is done. However, we have decided to add the mental state prior to the moment of decision of the subjects as a variable, see Figure 3. That is, the moment before making a decision. To do this, after make a signal processing and a feature extraction a classification of the mental tasks of various subjects with several classifiers of machine learning is done.
Figure 3.
Proposal workflow scheme.
Later this is repeated, but taking into account their IPMS to compare the results. It seeks to demonstrate that there is a significant improvement in pattern recognition taking into account the average rank of success in classification and the standard deviation. We expect that the success rate in classification will increase and the standard deviation to decrease. This study is focus not in the success rate per se, but in the difference between taking into account IPMS or not.
2.3 Development of the BCI system
2.3.1 Signal acquisition
To carry out this work, the signal has been acquired by downloading the public database Physionet.org. A database developed in collaboration with the developers of the BCI 2000 system [20, 21, 22]. The database consists of 109 users who perform different types of tests, obtaining more than 1500 EEG records. Using a 10/20 system and placing a total of 64 electrodes. The tests to be carried out consist of 14 tests of approximately 2 minutes per test, where the subjects alternate periods of 4.1 s of rest with different tasks (of between 4.1 and 4.2 s) such as opening and closing their fist or imagining these movements.
In order to prove the hypothesis, in this chapter, several simplifications have been done:
First. A group of 10 subjects is chosen as representative group of the set to demonstrate if there is evidence of success in having taken the IPMS into account.
Second. Only experiments involving actual and thought movement of the left hand and right hand will be dealt with. We believe that if the hypothesis is proven here, it can be concluded that the same thing happens in the rest of the experiments.
Third. According to studies, such as the one presented by Craik, A et al. [23], the motor activity associated with the hands is reflected in the frontal cortex of the brain and therefore the channels that contain major information are the 12 channels FC1, FC2, FC4, FC6, FCz, C1, C2 C3, C4, C5, C6 and Cz.
Finally. The nomenclature that these experiments follow will be T0 for the rest intervals. T1 for the real or imaginary movement of the left hand and T2 for the real or imaginary movement of the right hand.
Obtaining in this way labeled data matrices for the experiments having taken into account the IPMS and without taking it into account. To obtain the IPMS, a difference between the interaction intervals (T1/T2) and the rest intervals prior to the motor imagery (T0) is made.
2.3.2 Feature extraction and pattern recognition
After acquiring the signal, the next step when developing a BCI system is to perform an optimal feature extraction that allows good pattern recognition.
The first thing to do is remove the noise that masks the signal and thus obtain a better signal-to-noise ratio. This is because when taking biological measurements, factors such as breathing itself, the heartbeat (low frequencies) or the electricity that runs through the circuit (high frequencies) are factors that add noise and can mask the signal. Considering the frequency of brain signals (1–100 Hz), in the EEG a key factor is to remove these unnecessary frequencies. Therefore, based on multiple studies [24, 25, 26], a bandpass filter is applied between 0.5 Hz and 50 HZ in order to avoid the electrical 50 Hz band and the low frequencies.
After noise removal, the discrete wavelet transform (DWT) is used. DWT is a signal processing tool used to perform multi-resolution analysis with variable time windows. It is a tool capable of decomposing and recomposing signals according to their time and frequency to facilitate the analysis [27]. Brain signals have unpredictable frequency and intensity over time, so they are non-stationary. Using the DWT is capable of breaking a signal with low-pass and high-pass filters at different levels, see Figure 4. Thus, obtaining a high frequency component and a low frequency component (with different information) at various levels. These levels correspond to different types of brain waves (delta to beta) that provide different types of information and could facilitate the pattern recognition.
Figure 4.
Level 1 of decomposition using DWT. Original signal is separated in low and high frequency.
Mathematically, the equation behind the DWT is given by Eq. (1), [28]:
ft=∑k∑jaj,kφj,ktE1
This equation is expressed in terms of two indices, the translation time k and the scaling index j. These two indices are integers values and the wavelet functions form an orthogonal set of functions (base) [29].
Daubechies family of wavelet have a better performance at time of classification as is shown by Alomari et al. [30] and others [20, 21].
Feature extraction allows at this point to have a better signal-to-noise ratio in the labeled matrices obtained in Section 2.1.1 and also a separation based on their frequencies. Thus, allowing pattern recognition when applying machine learning to be less expensive in terms of computing. This is due motor activities requires an active state of mind, and in consequence the result are the alpha and beta waves (8–30 Hz) already separated.
2.3.3 Classification
A series of machine learning classifiers are used to recognize the patterns of motor imagery (MI) and classify them. Each of the users has 3 real experiments and 3 MI experiments.
To test the hypothesis, the classifiers will be used with the MI experiments. In this way, it will be observed if there is an improvement in performance when taking into account the IPMS. The hold-out cross validation method is used to train and test the success in classification, training each subject with 70% of the set and blindly testing with 30%. The classifiers used are:
We will not go into the mathematical details behind these classifiers as they are widely referenced and used in the world of pattern recognition and artificial intelligence. Attached is the reference where this information can be found.
As explained in Section 2.2.1 the method to evaluate the success when taking into account the IPMS is the average success of the results and its standard deviation. In Table 1, the classify results without taking into account IPMS:
Users
DT
LDA
LA
SVM
KNN
EM
NN
S001_exp1_1
33.3
40.0
40.0
60.0
60.0
46.7
60.0
S001_exp1_2
53.3
40.0
40.0
53.3
80.0
66.7
26.7
S001_exp1_3
66.7
53.3
73.3
73.3
73.3
86.7
46.7
S002_exp1_1
66.7
66.7
73.3
80.0
80.0
73.3
60.0
S002_exp1_2
80.0
66.7
46.7
66.7
80.0
80.0
66.7
S002_exp1_3
53.3
33.3
60.0
53.3
86.7
60.0
53.3
S003_exp1_1
20.0
40.0
53.3
60.0
53.3
53.3
26.7
S003_exp1_2
26.0
60.0
40.0
73.3
73.3
66.7
73.3
S003_exp1_3
33.3
60.0
53.3
53.3
66.7
60.0
53.3
S004_exp1_1
26.7
33.3
93.3
53.3
53.3
33.3
53.3
S004_exp1_2
66.7
73.3
66.7
73.3
80.0
73.3
73.3
S004_exp1_3
40.0
53.3
53.3
60.0
73.3
60.0
60.0
S005_exp1_1
73.3
60.0
60.0
93.3
80.0
73.3
26.7
S005_exp1_2
26.7
26.7
46.7
60.0
60.0
53.3
46.7
S005_exp1_3
53.3
60.0
53.3
73.3
73.3
60.0
46.7
S006_exp1_1
26.7
53.3
46.7
53.3
46.7
46.7
53.3
S006_exp1_2
53.3
40.0
86.7
53.3
80.0
66.7
66.7
S006_exp1_3
60.0
46.7
20.0
53.3
66.7
66.7
33.3
S007_exp1_1
40.0
53.3
60.0
60.0
73.3
73.3
73.3
S007_exp1_2
73.3
66.7
60.0
80.0
86.7
73.3
60.0
S007_exp1_3
40.0
33.3
40.0
53.3
60.0
53.3
26.7
S008_exp1_1
80.0
60.0
80.0
73.3
93.3
80.0
46.7
S008_exp1_2
13.3
46.7
86.7
66.7
53.3
53.3
60.0
S008_exp1_3
33.3
46.7
20.0
53.3
73.3
26.7
66.7
S009_exp1_1
66.7
46.7
66.7
53.3
66.7
73.3
53.3
S009_exp1_2
60.0
46.7
53.3
53.3
60.0
66.7
26.7
S009_exp1_3
73.3
46.7
40.0
53.3
73.3
73.3
73.3
S010_exp1_1
86.7
53.3
40.0
73.3
60.0
86.7
33.3
S010_exp1_2
40.0
33.3
46.7
53.3
53.3
53.3
33.3
S010_exp1_3
40.0
26.7
40.0
53.3
53.3
46.7
33.3
Table 1.
Classification without taking into account IPMS hypothesis.
In Table 2, the classify results taking into account IPMS:
Users
DT
LDA
LA
SVM
KNN
EM
NN
S001_exp1_11 1
40.0
33.3
53.3
66.7
53.3
53.3
60.0
S001_exp1_2
73.3
66.7
80.0
73.3
73.3
86.7
60.0
S001_exp1_3
53.3
33.3
73.3
60.0
66.7
73.3
33.3
S002_exp1_1
33.3
86.7
86.7
53.3
53.3
46.7
33.3
S002_exp1_2
73.3
60.0
46.7
86.7
86.7
73.3
46.7
S002_exp1_3
40.0
40.0
46.7
53.3
66.7
66.7
46.7
S003_exp1_1
46.7
46.7
20.0
53.3
73.3
53.3
60.0
S003_exp1_2
93.3
66.7
73.3
73.3
80.0
93.3
73.3
S003_exp1_3
80.0
46.7
60.0
60.0
60.0
80.0
40.0
S004_exp1_1
86.7
53.3
46.7
53.3
60.0
73.3
60.0
S004_exp1_2
60.0
46.7
53.3
53.3
60.0
60.0
53.3
S004_exp1_3
93.3
66.7
53.3
66.7
80.0
93.3
66.7
S005_exp1_1
60.0
60.0
53.3
66.7
73.3
53.3
46.7
S005_exp1_2
73.3
80.0
73.3
80.0
80.0
73.3
60.0
S005_exp1_3
40.0
40.0
40.0
53.3
53.3
46.7
33.3
S006_exp1_1
73.3
60.0
66.7
66.7
60.0
60.0
46.7
S006_exp1_2
80.0
73.3
80.0
80.0
80.0
86.7
80.0
S006_exp1_3
40.0
53.3
53.3
66.7
66.7
66.7
40.0
S007_exp1_1
60.0
46.7
66.7
53.3
86.7
60.0
46.7
S007_exp1_2
46.7
33.3
53.3
53.3
46.7
60.0
40.0
S007_exp1_3
66.7
40.0
46.7
60.0
86.7
86.7
66.7
S008_exp1_1
80.0
46.7
60.0
60.0
66.7
60.0
46.7
S008_exp1_2
73.3
73.3
93.3
86.7
80.0
93.3
40.0
S008_exp1_3
53.3
53.3
46.7
60.0
86.7
66.7
60.0
S009_exp1_1
73.3
60.0
66.7
73.3
73.3
66.7
53.3
S009_exp1_2
60.0
46.7
40.0
53.3
66.7
60.0
66.7
S009_exp1_3
60.0
33.3
40.0
53.3
66.7
60.0
46.7
S010_exp1_1
53.3
53.3
33.3
53.3
86.7
60.0
46.7
S010_exp1_2
86.7
46.7
53.3
60.0
73.3
73.3
60.0
S010_exp1_3
40.0
26.7
73.3
53.3
53.3
46.7
33.3
Table 2.
Classification taking into account IPMS hypothesis.
It must be remembered that what is sought is to observe an improvement in pattern recognition by implementing the IPMS hypothesis and we are not focused on the search for the best classifier.
After obtaining the results of the classifications with and without IPMS, a comparison of the average of both methods is made, which is shown in Table 3. It can be seen that in 100% of the cases the classification improves significantly, with results of up to 12% of improvement in classification. This leads us to think that for the recognition of brain patterns, taking into account the mental state prior to motor imagery is essential to obtain a better performance.
NO_IPMS
DT
LDA
LA
SVM
KNN
EM
NN
Avg_S001
51.1
44.4
51.1
62.2
71.1
66.7
44.5
Avg_S002
66.7
55.6
60.0
66.7
82.2
71.1
60.0
Avg_S003
26.4
53.3
48.9
62.2
64.4
60.0
51.1
Avg_S004
44.5
53.3
71.1
62.2
68.9
55.5
62.2
Avg_S005
51.1
48.9
53.3
75.5
71.1
62.2
40.0
Avg_S006
46.7
46.7
51.1
53.3
64.5
60.0
51.1
Avg_S007
51.1
51.1
53.3
64.4
73.3
66.6
53.3
Avg_S008
42.2
51.1
62.2
64.4
73.3
53.3
57.8
Avg_S009
66.7
46.7
53.3
53.3
66.7
71.1
51.1
Avg_S010
55.6
37.8
42.2
60.0
55.5
62.2
33.3
Total_Avg
51.1
48.6
53.8
62.2
68.6
63.9
49.6
STD
11.2
5.0
7.6
6.1
6.7
5.7
8.6
WITH_IPMS
DT
LDA
LA
SVM
KNN
EM
NN
Avg_S001
55.5
44.4
68.9
66.7
64.4
71.1
51.1
Avg_S002
48.9
62.2
60.0
64.4
68.9
62.2
42.2
Avg_S003
73.3
53.4
51.1
62.2
71.1
75.5
57.8
Avg_S004
80.0
55.6
51.1
57.8
66.7
75.5
60.0
Avg_S005
57.8
60.0
55.5
66.7
68.9
57.8
46.7
Avg_S006
64.4
62.2
66.7
71.1
68.9
71.1
55.6
Avg_S007
57.8
40.0
55.6
55.5
73.4
68.9
51.1
Avg_S008
68.9
57.8
66.7
68.9
77.8
73.3
48.9
Avg_S009
64.4
46.7
48.9
60.0
68.9
62.2
55.6
Avg_S010
60.0
42.2
53.3
55.5
71.1
60.0
46.7
Total_Avg
63.1
52.4
57.8
62.9
70.0
67.8
51.6
STD
8.7
8.0
7.0
5.3
3.5
6.3
5.3
Success rate with IPMS
DT
LDA
LA
SVM
KNN
EM
NN
Avg_Change (%)
12.0
3.8
4.0
0.7
1.4
3.9
2.0
STD_Change (%)
2.5
−3.0
0.6
0.8
3.2
−0.6
3.2
Table 3.
Comparison between NO IPMS and IPMS classification. Avg change and standard deviation changes of both methods.
In this chapter a brief introduction to the field of brain signals has been made. The pattern recognition for the development of applications in fields such as medicine or industry and how to analyze brain signals for that propose has been explained. Subsequently, the BCIs have been introduced, explaining their operation and purpose. The IPMS hypothesis is proposed as an improvement of pattern recognition in BCI systems.
A public database (Physionet), where EEG records of subjects performing a series of motor and imaginary tasks are collected, is presented and using this dataset the steps to develop a BCI system are proposed. The signal is processed, features are extracted for pattern recognition and finally a series of classifiers are proposed to test the IPMS theory.
The results show there is evidence that taking into account the mental state prior to performing mental tasks directly affects the recognition of brain patterns and consequently the success in classifying them, improving them by up to 12%.
Note that research has been focused on testing this hypothesis and not on finding the best classifier. In future lines, we will try to apply the IPMS hypothesis to the best state-of-the-art classifiers.
This work was funded “Agencia Canaria de Investigación, Innovación y Sociedad de la Información de la Consejería de Economía Conocimiento y Empleo y por el Fondo Social Europeo (FSE) Programa Operativo Integrado de Canarias 2014-2020, Eje 3 Tema Prioritario 74 (85%)” from “Gobierno de Canarias” in Spain, under the reference “TESIS2020010118”.
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Written By
Nabil Ajali-Hernández and Carlos M. Travieso-Gonzalez
Submitted: 30 April 2022Reviewed: 08 August 2022Published: 09 September 2022
Open access peer-reviewed chapter
