scholarly journals Exploring the Attention Process Differentiation of ADHD Symptomatic Adults Using Artificial Intelligence on EEG Signals

2021 ◽  
Keyword(s):  
Artificial Intelligence ◽  
Eeg Signals ◽  
Attention Process

scholarly journals Brain-Inspired Healthcare Smart System Based on Perception-Action Cycle

2020 ◽  
Vol 10 (10) ◽  
pp. 3532
Author(s):  
Jesús Jaime Moreno Escobar ◽  
Oswaldo Morales Matamoros ◽  
Ricardo Tejeida Padilla ◽  
Ixchel Lina Reyes ◽  
Liliana Chanona Hernández ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Medical Doctors ◽  
Eeg Signals ◽  
Smart System ◽  
Brain Signals ◽  
Efficiency Assessment ◽  
Power Spectral ◽  
Electroencephalogram Eeg ◽  
Perception Action

This work presents the HSS-Cognitive project, which is a Healthcare Smart System that can be applied in measuring the efficiency of any therapy where neuronal interaction gives a trace whether the therapy is efficient or not, using mathematical tools. The artificial intelligence of the project underlies in the understanding of brain signals or Electroencephalogram (EEG) by means of the determination of the Power Spectral Density (PSD) over all the EEG bands in order to estimate how efficient was a therapy. Our project HSS-Cognitive was applied, recording the EEG signals from two patients treated for 8 min in a dolphin tank, measuring their activity in five experiments and for 6 min measuring their activity in a pool without dolphin in four experiments. After applying our TEA (Therapeutic Efficiency Assessment) metric for patient 1, we found that this patient had gone from having relaxation states regardless of the dolphin to attention states when the dolphin was presented. For patient 2, we found that he had maintained attention states regardless of the dolphin, that is, the DAT (Dolphin Assisted Therapy) did not have a significant effect in this patient, perhaps because he had a surgery last year in order to remove a tumor, having impact on the DAT effectiveness. However, patient 2 presented the best efficiency when doing physical therapy led by a therapist in a pool without dolphins around him. According to our findings, we concluded that our Brain-Inspired Healthcare Smart System can be considered a reliable tool for measuring the efficiency of a dolphin-assisted therapy and not only for therapist or medical doctors but also for researchers in neurosciences.

scholarly journals SCENE AND OBJECT CLASSIFICATION USING BRAIN WAVES SIGNAL

2017 ◽  
Vol 10 (13) ◽  
pp. 137
Author(s):  
Darshan A Khade ◽  
Ilakiyaselvan N
Keyword(s):  
Artificial Intelligence ◽  
Event Related Potentials ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Eeg Signals ◽  
Future Technology ◽  
Brain Waves ◽  
Brain Signals ◽  
Related Potentials ◽  
Signal Machine

This study aims to classify the scene and object using brain waves signal. The dataset captured by the electroencephalograph (EEG) device by placing the electrodes on scalp to measure brain signals are used. Using captured EEG dataset, classifying the scene and object by decoding the changes in the EEG signals. In this study, independent component analysis, event-related potentials, and grand mean are used to analyze the signal. Machine learning algorithms such as decision tree, random forest, and support vector machine are used to classify the data. This technique is useful in forensic as well as in artificial intelligence for developing future technology. 

scholarly journals Noise Robustness Low-Rank Learning Algorithm for Electroencephalogram Signal Classification

2021 ◽  
Vol 15 ◽  
Author(s):  
Ming Gao ◽  
Runmin Liu ◽  
Jie Mao
Keyword(s):  
Artificial Intelligence ◽  
Learning Algorithm ◽  
Original Data ◽  
Electrical Signal ◽  
Signal Classification ◽  
Low Rank ◽  
Noise Robustness ◽  
Support Vector ◽  
Eeg Signals ◽  
Data Set

Electroencephalogram (EEG) is often used in clinical epilepsy treatment to monitor electrical signal changes in the brain of patients with epilepsy. With the development of signal processing and artificial intelligence technology, artificial intelligence classification method plays an important role in the automatic recognition of epilepsy EEG signals. However, traditional classifiers are easily affected by impurities and noise in epileptic EEG signals. To solve this problem, this paper develops a noise robustness low-rank learning (NRLRL) algorithm for EEG signal classification. NRLRL establishes a low-rank subspace to connect the original data space and label space. Making full use of supervision information, it considers the local information preservation of samples to ensure the low-rank representation of within-class compactness and between-classes dispersion. The asymmetric least squares support vector machine (aLS-SVM) is embedded into the objective function of NRLRL. The aLS-SVM finds the maximum quantile distance between the two classes of samples based on the pinball loss function, which further improves the noise robustness of the model. Several classification experiments with different noise intensity are designed on the Bonn data set, and the experiment results verify the effectiveness of the NRLRL algorithm.

Design of Assistive Speller Machine Based on Brain Computer Interfacing

2017 ◽  
pp. 385-418
Author(s):  
Suryoday Basak
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Signal Processing ◽  
Data Analysis ◽  
Brain Computer Interface ◽  
Classification Methods ◽  
Eeg Signals ◽  
Signal Processing Methods ◽  
Brain Computer Interfacing

Machine Learning (ML) has assumed a central role in data assimilation and data analysis in the last decade. Many methods exist that cater to the different kinds of data centric applications in terms of complexity and domain. Machine Learning methods have been derived from classical Artificial Intelligence (AI) models but are a lot more reliant on statistical methods. However, ML is a lot broader than inferential statistics. Recent advances in computational neuroscience has identified Electroencephalography (EEG) based Brain Computer Interface (BCI) as one of the key agents for a variety of medical and nonmedical applications. However, efficiency in analysing EEG signals is tremendously difficult to achieve because of three reasons: size of data, extent of computation and poor spatial resolution. The book chapter discusses the Machine Learning based methods employed by the author to classify EEG signals for potentials observed based on varying levels of a subject's attention, measured using a NeuroSky Mindwave Mobile. It reports challenges faced in developing BCIs based on available hardware, signal processing methods and classification methods.

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