Brain-Controlled for Changing Modular Robot Configuration by Employing Neurosky’s Headset

Hasbulah, Muhammad Haziq; Jafar, Fairul Azni; Nordin, M.H.; Yokota, Kazutaka

doi:10.14569/ijacsa.2019.0100617

Cited by 4 publications

(5 citation statements)

References 19 publications

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“…This was not surprising, since CSP features, which can be used to extract spatial features from EEG time series, have a long history as robust features in MI EEG classification [ 78 ], and 80% of the reviewed studies used an MI-based paradigm. Many studies used traditional CSP features [ 29 , 37 , 43 , 44 , 51 , 53 , 54 , 55 , 63 , 66 ], which were established for MI EEG classification [ 78 ]. However, filter bank CSP features, which have been shown to perform better than traditional CSP features [ 79 ], were also common [ 35 , 46 , 47 ].…”

Section: Signal-processing and Classification Techniques At The Cutti...mentioning

confidence: 99%

A Comprehensive Review of Endogenous EEG-Based BCIs for Dynamic Device Control

Padfield

Camilleri

et al. 2022

Sensors

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Electroencephalogram (EEG)-based brain–computer interfaces (BCIs) provide a novel approach for controlling external devices. BCI technologies can be important enabling technologies for people with severe mobility impairment. Endogenous paradigms, which depend on user-generated commands and do not need external stimuli, can provide intuitive control of external devices. This paper discusses BCIs to control various physical devices such as exoskeletons, wheelchairs, mobile robots, and robotic arms. These technologies must be able to navigate complex environments or execute fine motor movements. Brain control of these devices presents an intricate research problem that merges signal processing and classification techniques with control theory. In particular, obtaining strong classification performance for endogenous BCIs is challenging, and EEG decoder output signals can be unstable. These issues present myriad research questions that are discussed in this review paper. This review covers papers published until the end of 2021 that presented BCI-controlled dynamic devices. It discusses the devices controlled, EEG paradigms, shared control, stabilization of the EEG signal, traditional machine learning and deep learning techniques, and user experience. The paper concludes with a discussion of open questions and avenues for future work.

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Section: Signal-processing and Classification Techniques At The Cutti...mentioning

confidence: 99%

A Comprehensive Review of Endogenous EEG-Based BCIs for Dynamic Device Control

Padfield

Camilleri

et al. 2022

Sensors

View full text Add to dashboard Cite

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“…A range of commercially available devices is available, some of which offer acceptable levels of accuracy and ease of wear [61]. Among them are MindWave Mobile 2 (NeuroSky) [62] and Emotiv Insight (Emotiv), with the latter having been used for several research studies [63]. In any case, these devices are still used only with a research purpose, which certainly introduces a limitation in a real life use.…”

Section: Brain Activitymentioning

confidence: 99%

An Autonomous Alarm System for Personal Safety Assurance of Intimate Partner Violence Survivors Based on Passive Continuous Monitoring through Biosensors

et al. 2020

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Intimate Partner Violence (IPV) dramatically compromises the free and complete development of many women around the world, therefore leading to social asymmetry regarding the right to personal safety. In many cases, a woman who has reported her partner to police for gender-based violence needs to ensure her protection (either before the trial of the aggressor or after their freedom). Thus, it would be ideal if autonomous alarm systems could be developed in order to call the police if necessary. Up to now, many proposals have been presented in this regard, including solutions based on Information and Communication Technologies (ICT) but, unfortunately, these approaches usually rely on the active participation of the victims (survivors), who have to turn the system on by themselves if needed. Therefore, in order to overcome such limitations, in this work, a passive continuous monitoring system is proposed which uses biosensors attached to the survivor as well as machine learning techniques to infer if an abnormal situation related to gender-based violence is taking place, activating in this case an alarm. The monitoring structure of the system supervises a great deal of bio-signals according to the current status of technology of wearables and biomedical devices. The presented biosensors-based surveillance solution can also be manually disconnected for 30/60/90 min (on demand) in order to avoid false positives when a woman is, for example, practicing sports or carrying out other inoffensive activities that could incorrectly activate the alarm.

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“…Two sensors are used to operate this device to obtain and filter EEG signals. The sensor tip locates on the forehead and detects electrical signals emanating from the brain's frontal lobe [19,20]. The second sensor is an ear clip, which is used as a ground acting as a filter for electrical noise [21].…”

Section: Eeg Signal Acquisitionmentioning

confidence: 99%

Brain computer interface based smart keyboard using neurosky mindwave headset

Salih

Abdal

2020

TELKOMNIKA

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In the last decade, numerous researches in the field of electro-encephalography (EEG) and brain-computer-interface (BCI) have been accomplished. BCI has been developed to aid disabled/partially disabled people to efficiently communicate with the community. This paper presents a control tool using the Neurosky Mindwave headset, which detects brainwaves (voluntary blinks and attention) to form a braincomputer interface (BCI) by receiving the system signals from the frontal lobe. This paper proposed an alternative computer input device for those disabled people (who are physically challenged) rather than the conventional one. The work suggested to use two virtual keyboard designs. The conducted experiment revealed a significant result in developing user printing skills on PCs. Encouraging results (1.55-1.8 word per minute (WPM)) were obtained in this research in comparison to other studies.

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Brain-Controlled for Changing Modular Robot Configuration by Employing Neurosky’s Headset

Cited by 4 publications

References 19 publications

A Comprehensive Review of Endogenous EEG-Based BCIs for Dynamic Device Control

A Comprehensive Review of Endogenous EEG-Based BCIs for Dynamic Device Control

An Autonomous Alarm System for Personal Safety Assurance of Intimate Partner Violence Survivors Based on Passive Continuous Monitoring through Biosensors

Brain computer interface based smart keyboard using neurosky mindwave headset

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