EEG-Based BCI for Household IoT Control
| dc.contributor.author | Dunn, Bryce | |
| dc.date.accessioned | 2021-10-08T20:22:19Z | |
| dc.date.available | 2021-10-08T20:22:19Z | |
| dc.date.issued | 2021 | |
| dc.description.abstract | Brain-computer interface (BCI) paradigms have existed for decades to improve communication and technological control. Electroencephalography (EEG) represents one of the most common non-invasive approaches toward recording brain signals in human participants [1]. Many features within EEG signal are used to model BCI including five major types of brain wave frequency bands, autoregressive parameters, and power spectral density values [2]. Processing EEG to produce a desired output demands signal filtration, feature extraction and classification. Approach. Using the Emotiv EPOC+ headset, electrodes placed on the scalp record passive mental activity at centimeter resolution [3]. The EPOC+ represents a low-cost alternative to medical-grade hardware, which may allow the development of this platform to be more accessible to end-users. Filtration and classification methods are applied to distinguish signal frequencies of interest. The recorded EEG signal is used to demonstrate potential for passive control a simple household appliance such as a light fixture. To this end, a participant’s passive EEG is recorded during a series of tasks in varying light settings. In addition to technical challenges, there are practical considerations to overcome, such as variability and subject fatigue. Preliminary results suggest a measurable distinction in mental state between tasks. Significance. Following this line of inquiry, a platform for interfacing with the increasingly ubiquitous internet of things (IoT) may be developed in the future. The potential applications of BCI are myriad and promise to better living conditions by enhancing and supplementing central nervous system output. EEG-based signaling may provide means to greater autonomy and technological accessibility for disabled people and patients with neurological deficits. | |
| dc.identifier.uri | https://hdl.handle.net/1920/12115 | |
| dc.language.iso | en_US | |
| dc.rights | Attribution-ShareAlike 3.0 United States | |
| dc.rights.uri | https://creativecommons.org/licenses/by-sa/3.0/us/ | |
| dc.title | EEG-Based BCI for Household IoT Control | |
| dc.type | Working Paper |