An Innovative Tool to Create Neurofeedback Games for ADHD Treatment

Teruel, Miguel A.; Navarro, Elena; Romero, Dulce; García, M. Nieves Sánchez; Fernández-Caballero, Antonio; González, Pascual

doi:10.1007/978-3-319-59740-9_18

Cited by 11 publications

(7 citation statements)

References 14 publications

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“…The rapid increase in the number of publications concerning Brain-Computer Interfaces (BCI) is observed (Mikołajewska and Mikołajewski, 2012, 2013, 2014; Teruel et al, 2017; Ozga et al, 2018; Wierzgała et al, 2018) and the EEG activity can be recognized as one of possible solutions in BCI engineering (Kotyra and Wojcik, 2017a,b). In addition, any ideas for finding biomarkers of psychiatric disorders (Chapman and Bragdon, 1964; Sutton et al, 1965; Campanella, 2013; Golonka et al, 2017) are in demand as the interview is still the most often used tool in psychiatry to make the diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Decision-Making Process Using Methods of Quantitative Electroencephalography and Machine Learning Tools

Wójcik

Masiak

Kawiak

et al. 2019

Front. Neuroinform.

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The electroencephalographic activity of particular brain areas during the decision making process is still little known. This paper presents results of experiments on the group of 30 patients with a wide range of psychiatric disorders and 41 members of the control group. All subjects were performing the Iowa Gambling Task that is often used for decision process investigations. The electroencephalographical activity of participants was recorded using the dense array amplifier. The most frequently active Brodmann Areas were estimated by means of the photogrammetry techniques and source localization algorithms. The analysis was conducted in the full frequency as well as in alpha, beta, gamma, delta, and theta bands. Next the mean electric charge flowing through each of the most frequently active areas and for each frequency band was calculated. The comparison of the results obtained for the subjects and the control groups is presented. The difference in activity of the selected Brodmann Areas can be observed in all variants of the task. The hyperactivity of amygdala is found in both the patients and the control group. It is noted that the somatosensory association cortex, dorsolateral prefrontal cortex, and primary visual cortex play an important role in the decision-making process as well. Some of our results confirm the previous findings in the fMRI experiments. In addition, the results of the electroencephalographic analysis in the broadband as well as in specific frequency bands were used as inputs to several machine learning classifiers built in Azure Machine Learning environment. Comparison of classifiers' efficiency is presented to some extent and finding the most effective classifier may be important for planning research strategy toward finding decision-making biomarkers in cortical activity for both healthy people and those suffering from psychiatric disorders.

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Section: Introductionmentioning

confidence: 99%

Analysis of Decision-Making Process Using Methods of Quantitative Electroencephalography and Machine Learning Tools

Wójcik

Masiak

Kawiak

et al. 2019

Front. Neuroinform.

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“…Electrophysiological methods have developed in recent decades (Kamarajan and Porjesz, 2015 ; Martínez-Rodrigo et al, 2017 ). Recently there has been a rapid advance in therapeutic use of Brain-Computer Interfaces (BCI) in which the acquisition of electrical activity of selected areas of brain cortex plays the main role (Mikołajewska and Mikołajewski, 2012 , 2013 , 2014 ; Teruel et al, 2017 ) and Event-Related Potentials (ERP) and other evoked potentials can lead not only to explanation of psychological behaviors in particular situations (Kotyra and Wojcik, 2017a , b ) but also to finding some biomarkers characteristic of psychiatric disorders (Chapman and Bragdon, 1964 ; Sutton et al, 1965 ; Campanella, 2013 ; Golonka et al, 2017 ). Together with the development of neurocomputing, neuroinformatics and artificial intelligence a lot of new tools and possibilities appeared and made their use possible for a wide range of classification tasks in biomedical engineering (Ogiela et al, 2008 ; Szaleniec et al, 2008 , 2013 ) or brain functions simulations which are also a subject of our investigations (Ważny and Wojcik, 2014 ; Wojcik and Ważny, 2015 ).…”

Section: Introductionmentioning

confidence: 99%

Mapping the Human Brain in Frequency Band Analysis of Brain Cortex Electroencephalographic Activity for Selected Psychiatric Disorders

Wójcik

Masiak

Kawiak

et al. 2018

Front. Neuroinform.

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There are still no good quantitative methods to be applied in psychiatric diagnosis. The interview is still the main and most important tool in the psychiatrist work. This paper presents the results of electroencephalographic research with the subjects of a group of 30 patients with psychiatric disorders compared to the control group of healthy volunteers. All subjects were solving working memory task. The digit-span working memory task test was chosen as one of the most popular tasks given to subjects with cognitive dysfunctions, especially for the patients with panic disorders, depression (including the depressive phase of bipolar disorder), phobias, and schizophrenia. Having such cohort of patients some results for the subjects with insomnia and Asperger syndrome are also presented. The cortical activity of their brains was registered by the dense array EEG amplifier. Source localization using the photogrammetry station and the sLORETA algorithm was then performed in five EEG frequency bands. The most active Brodmann Areas are indicated. Methodology for mapping the brain and research protocol are presented. The first results indicate that the presented technique can be useful in finding psychiatric disorder neurophysiological biomarkers. The first attempts were made to associate hyperactivity of selected Brodmann Areas with particular disorders.

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“…Neurofeedback technology is one of the behavioral, non-pharmacological remedies, which has started to become popular because of its promising effectiveness in disorder treatment and the improvement of the patient's health. Many studies [125,[137][138][139][140][141][142][143][144][145][146] presented the concept of neurofeedback training and showed that it gave a treatment option for patients, a decreasing in the fundamental symptoms such as in the cases of cerebral palsy [147], stroke [148], and ADHD [149][150][151][152]. Generally, the treatment consists of the patient's request to wear a BCI headset and perform tasks specifies by a trainer.…”

Section: Gamingmentioning

confidence: 99%

Wireless Sensors for Brain Activity—A Survey

et al. 2020

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Over the last decade, the area of electroencephalography (EEG) witnessed a progressive move from high-end large measurement devices, relying on accurate construction and providing high sensitivity, to miniature hardware, more specifically wireless wearable EEG devices. While accurate, traditional EEG systems need a complex structure and long periods of application time, unwittingly causing discomfort and distress on the users. Given their size and price, aside from their lower sensitivity and narrower spectrum band(s), wearable EEG devices may be used regularly by individuals for continuous collection of user data from non-medical environments. This allows their usage for diverse, nontraditional, non-medical applications, including cognition, BCI, education, and gaming. Given the reduced need for standardization or accuracy, the area remains a rather incipient one, mostly driven by the emergence of new devices that represent the critical link of the innovation chain. In this context, the aim of this study is to provide a holistic assessment of the consumer-grade EEG devices for cognition, BCI, education, and gaming, based on the existing products, the success of their underlying technologies, as benchmarked by the undertaken studies, and their integration with current applications across the four areas. Beyond establishing a reference point, this review also provides the critical and necessary systematic guidance for non-medical EEG research and development efforts at the start of their investigation.

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An Innovative Tool to Create Neurofeedback Games for ADHD Treatment

Cited by 11 publications

References 14 publications

Analysis of Decision-Making Process Using Methods of Quantitative Electroencephalography and Machine Learning Tools

Analysis of Decision-Making Process Using Methods of Quantitative Electroencephalography and Machine Learning Tools

Mapping the Human Brain in Frequency Band Analysis of Brain Cortex Electroencephalographic Activity for Selected Psychiatric Disorders

Wireless Sensors for Brain Activity—A Survey

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