Analysis of EEG to Find Alzheimer’s Disease Using Intelligent Techniques

Gurumoorthy, Sasikumar; Muppalaneni, Naresh Babu; Gao, Xueshan

doi:10.1007/978-981-10-6529-3_5

Cited by 3 publications

(2 citation statements)

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“…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].…”

Section: Pattern Recognition With Immediate Previous Mental State 21 ...mentioning

confidence: 99%

“…These impulses could be classified by their frequency into different types of brain waves. Delta (1-3 Hz), theta (3)(4)(5)(6)(7)(8), alpha (8)(9)(10)(11)(12)(13), beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), 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.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of Brain Computer Interface Using Deep and Machine Learning

Ajali-Hernández¹,

González²

2022

Artificial Intelligence Annual Volume 2022

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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.

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