Cognitive brain–Computer interface and probable aspects of its practical application

Atanov, Michael S; Ivanitsky, G. A.; Ivanitsky, Alexey

doi:10.1134/s0362119716030038

Cited by 3 publications

(5 citation statements)

References 13 publications

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“…Ivanitsky et.al. figured out an important role of BCI feedback even for mastering complex cognitive tasks [14]. Ideally, a feedback should give a subject sufficient information about his progress, but not distract him from performing the mental task itself, i.e.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Natural image reconstruction from brain waves: a novel visual BCI system with native feedback

Rashkov¹,

Bobe²,

Fastovets

et al. 2019

Preprint

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Here we hypothesize that observing the visual stimuli of different categories trigger distinct brain states that can be decoded from noninvasive EEG recordings. We introduce an effective closed-loop BCI system that reconstructs the observed or imagined stimuli images from the co-occurring brain wave parameters. The reconstructed images are presented to the subject as a visual feedback. The developed system is applicable to training BCI-naïve subjects because of the user-friendly and intuitive way the visual patterns are employed to modify the brain states.

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Section: Introduction and Related Workmentioning

confidence: 99%

Natural image reconstruction from brain waves: a novel visual BCI system with native feedback

Rashkov¹,

Bobe²,

Fastovets

et al. 2019

Preprint

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“…Artificial neural networks, one of the widely used classification algorithms in passive BCI, showed remarkable efficiency (more than 90% accuracy) in recogni tion of mental operations basing on EEG spectral features while solving verbal and spatial tasks (Ivanitsky, 1997;Tarotin et al, 2017;Atanov, Ivanitsky, & Ivanitsky, 2016). Several studies have demonstrated a potential of EEG as an indicator of cognitive workload during training Antonenko & Niederhauser, 2010;Fairclough et al, 2005;Fairclough, Gilleade, Ewing, & Roberts, 2013;Zhu et al, 2010), which is defined as a perceived relationship between existing mental abilities and resources required for mental tasks (Hart & Staveland, 1988).…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, we com bined data from the current and previous datasets in order to study group differ ences in behavioral accuracy and time of task solution between math and humanitarian subjects. For the purpose of EEG classification we used an artificial neuronal network -the perceptron classifier without hidden layers (McCulloch & Pitts, 1943;Pitts & McCulloch, 1947), which was previously successfully imple mented for classification of EEG patterns during solving of verbal and spatial tasks (Ivanitsky, 1997;Atanov et al, 2016;Tarotin et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Классификация Вербальных И Математических Ментальных Операций На Основе Спектральной Плотности Мощности ЭЭГ

Чемерисова¹,

Атанов²,

Михеев³

et al. 2018

psychology

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A classification of spectral patterns of EEG underlies several cognitive neurotechnologies including passive and active brain-com puter interfaces. Despite arithm etic tasks often being used in studies of cognitive workload, there is a lack of findings describing a possibility to recognize EEG patterns related to different types of m ath operations. In th e present work, we have shown th a t th e power spectral density of EEG can be used to classify types of m ental operations including a classification of verbal and different m athem atical tasks for simple arithm etic operations or logical tasks w ith arithm etic progressions. The verbal tasks were separated from arithm etic ones significantly b etter th an arithm etic from logical tasks, and verbal from logical tasks. B etter discrim ination of verbal tasks from arithm etic b u t n ot from logical tasks supports the hypothesis of unique EEG patterns associated w ith verbal activity th a t apparently differ from m ental operations in arithm etic. Additionally, we compared th e behavioral performance in problem solving and accuracy of EEG classification in tw o groups of subjects w ith education in m ath or hum anities (N = 8 + 8). We obtained th e predicted differences related to b etter performance of th e m ath group in solving m ath tasks th an th e hum anitarian group. However, the classification accuracy of tasks based on EEG did n ot differ significantly between groups and was essentially higher th an random. Considered together, our results support the hypothesis th a t EEG patterns reflect individual cognitive states corresponding to m ental operations and can be used in classification of different cognitive activity.

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“…Artificial neural networks, one of the widely used classification algorithms in passive BCI, showed remarkable efficiency (more than 90% accuracy) in recognition of mental operations basing on EEG spectral features while solving verbal and spatial tasks (Ivanitsky, 1997;Tarotin et al, 2017;Atanov, Ivanitsky, & Ivanitsky, 2016). Several studies have demonstrated a potential of EEG as an indicator of cognitive workload during training Antonenko & Niederhauser, 2010;Fairclough et al, 2005;Fairclough, Gilleade, Ewing, & Roberts, 2013;Zhu et al, 2010), which is defined as a perceived relationship between existing mental abilities and resources required for mental tasks (Hart & Staveland, 1988).…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, we combined data from the current and previous datasets in order to study group differences in behavioral accuracy and time of task solution between math and humanitarian subjects. For the purpose of EEG classification we used an artificial neuronal network -the perceptron classifier without hidden layers (McCulloch & Pitts, 1943;Pitts & McCulloch, 1947), which was previously successfully implemented for classification of EEG patterns during solving of verbal and spatial tasks (Ivanitsky, 1997;Atanov et al, 2016;Tarotin et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

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Pokhoday¹,

Myachykov²

2018

psychology

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Учре ди тель Национальный исследовательский универ си тет «Вы сшая шко ла эко но ми ки» Главный редактор В.А. Петровский (НИУ ВШЭ) Редакционная коллегия Дж. Берри (Университет Куинс, Кана да) Г.М. Бреслав (Балтийская международная академия, Латвия) Я. Вальсинер (Ольборгский университет, Дания) Е.Л. Григоренко (МГУ им. М.В. Ло мо носова и Центр ребенка Йельского университета, США) В.А. Ключарев (НИУ ВШЭ) Д.А. Леонтьев (НИУ ВШЭ и МГУ им. М.В. Ло моносова) М. Линч (Рочестерский университет, США) Д.В. Люсин (НИУ ВШЭ и ИП РАН) Е.Н. Осин (НИУ ВШЭ) А.Н. Поддьяков (НИУ ВШЭ) Д.В. Ушаков (зам. глав. ред.) (ИП РАН) М.В. Фаликман (НИУ ВШЭ) А.В. Хархурин (Американский университет Шарджи, ОАЭ) В.Д. Шадриков (зам. глав. ред.) (НИУ ВШЭ) С.Р. Яголковский (зам. глав. ред.) (НИУ ВШЭ) Экспертный совет К.А. Абульханова-Славская (НИУ ВШЭ и ИП РАН) Н.А. Алмаев (ИП РАН) В.А. Барабанщиков (ИП РАН и МГППУ) Т.Ю. Базаров (НИУ ВШЭ и МГУ им. М.В. Ло моносова) А.К. Болотова (НИУ ВШЭ) А.Н. Гусев (МГУ им. М.В. Ломоносова) А.Л. Журавлев (ИП РАН) А.В. Карпов (Ярославский государственный университет им. П.Г. Демидова) А. Лэнгле (НИУ ВШЭ) А.Б. Орлов (НИУ ВШЭ) В.Ф. Петренко (МГУ им. М.В. Ломо но сова) В.М. Розин (ИФ РАН) И.H. Семенов (НИУ ВШЭ) Е.А. Сергиенко (ИП РАН) Е.Б. Старовойтенко (НИУ ВШЭ) Т.Н. Ушакова (ИП РАН) А.М. Черноризов (МГУ им. М.В. Ломо но со ва) А.Г. Шмелев (МГУ им. М.В. Ломо но сова) П. Шмидт (НИУ ВШЭ и Гиссенский университет, Германия)

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Cognitive brain–Computer interface and probable aspects of its practical application

Cited by 3 publications

References 13 publications

Natural image reconstruction from brain waves: a novel visual BCI system with native feedback

Natural image reconstruction from brain waves: a novel visual BCI system with native feedback

Классификация Вербальных И Математических Ментальных Операций На Основе Спектральной Плотности Мощности ЭЭГ

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