Computational EEG Analysis Techniques When Playing Video Games: A Systematic Review

Cabañero, Luis; Hervás, Ramón; Bravo, José; Rodríguez-Benítez, Luis

doi:10.3390/proceedings2190483

Cited by 12 publications

(6 citation statements)

References 26 publications

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“…Furthermore, innovations in computer vision using deep learning models, such as DeepLab Cut ( Mathis et al, 2018 ) require minimal training and should be able to accurately track task performance in individuals. Unsupervised learning models and dimensionality reduction methods can help us develop unbiased behavioral and neural insights into these multimodal recordings without a priori hypotheses or pre-defined features of interest ( Wang et al, 2016 ; Cabañero-Gómez et al, 2018 ; Hamilton and Huth, 2020 ). While many video games may be considered to be less statistically powerful ( Matusz et al, 2019 ), video games have a greater natural effect size, meaning they add external and content validity alongside the detection and importance of an effect.…”

Section: Discussionmentioning

confidence: 99%

A Platform for Cognitive Monitoring of Neurosurgical Patients During Hospitalization

Ashmaig

Hamilton

Modur

et al. 2021

Front. Hum. Neurosci.

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Intracranial recordings in epilepsy patients are increasingly utilized to gain insight into the electrophysiological mechanisms of human cognition. There are currently several practical limitations to conducting research with these patients, including patient and researcher availability and the cognitive abilities of patients, which limit the amount of task-related data that can be collected. Prior studies have synchronized clinical audio, video, and neural recordings to understand naturalistic behaviors, but these recordings are centered on the patient to understand their seizure semiology and thus do not capture and synchronize audiovisual stimuli experienced by patients. Here, we describe a platform for cognitive monitoring of neurosurgical patients during their hospitalization that benefits both patients and researchers. We provide the full specifications for this system and describe some example use cases in perception, memory, and sleep research. We provide results obtained from a patient passively watching TV as proof-of-principle for the naturalistic study of cognition. Our system opens up new avenues to collect more data per patient using real-world behaviors, affording new possibilities to conduct longitudinal studies of the electrophysiological basis of human cognition under naturalistic conditions.

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Section: Discussionmentioning

confidence: 99%

A Platform for Cognitive Monitoring of Neurosurgical Patients During Hospitalization

Ashmaig

Hamilton

Modur

et al. 2021

Front. Hum. Neurosci.

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“…To collect the EEG raw data, we used the Xavier TechBench Software TM . For the EEG data processing, there is a specialized software developed to process EEG data, called eeglib, which can also applied to other kind of data sources [26,27]. eeglib is actually a Python-based library for EEG processing that provides some data structures to help for that purpose.…”

Section: Methodsmentioning

confidence: 99%

Analysis of Cognitive Load Using EEG when Interacting with Mobile Devices

Cabañero

Hervás

Gónzalez

et al. 2019

13th International Conference on Ubiquitous Computing and Ambient ‪Intelligence UCAmI 2019‬

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The study of cognitive responses and processes while using applications is a critical field in human-computer interaction. This paper aims to determine the mental effort required for different typical tasks with smartphones. Mental effort is typically associated with the concept of cognitive load, and has been studied by analyzing electroencephalography (EEG) signals. Thus, this paper shows the results of analyzing the cognitive load of a set of characteristic tasks on smartphones. To determine the set of tasks to analyze, this paper proposes a taxonomy of smartphone-based actions defined after considering the related proposals in the literature and identifying the significant characteristics of the tasks to classify them. The EEG data was obtained through an experiment with real users doing tasks from the aforementioned taxonomy. The results show significant differences in the cognitive load of each task category and identify those tasks that involve a higher degree of mental effort. The results will be the starting point of the M 4 S project that aims to contribute to the early diagnosis of mild cognitive impairment through monitoring everyday dual-tasking in terms of interaction with smartphones.

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“…BCIs are increasingly being used in gaming applications where players can interact with virtual environments using only their thoughts instead of traditional controllers such as keyboards and joysticks [137][138][139][140][141][142].…”

Section: Gamingmentioning

confidence: 99%

State-of-the-Art on Brain-Computer Interface Technology

Pekša

Mamchur

2023

Sensors

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This paper provides a comprehensive overview of the state-of-the-art in brain–computer interfaces (BCI). It begins by providing an introduction to BCIs, describing their main operation principles and most widely used platforms. The paper then examines the various components of a BCI system, such as hardware, software, and signal processing algorithms. Finally, it looks at current trends in research related to BCI use for medical, educational, and other purposes, as well as potential future applications of this technology. The paper concludes by highlighting some key challenges that still need to be addressed before widespread adoption can occur. By presenting an up-to-date assessment of the state-of-the-art in BCI technology, this paper will provide valuable insight into where this field is heading in terms of progress and innovation.

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Computational EEG Analysis Techniques When Playing Video Games: A Systematic Review

Cited by 12 publications

References 26 publications

A Platform for Cognitive Monitoring of Neurosurgical Patients During Hospitalization

A Platform for Cognitive Monitoring of Neurosurgical Patients During Hospitalization

Analysis of Cognitive Load Using EEG when Interacting with Mobile Devices

State-of-the-Art on Brain-Computer Interface Technology

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