Scalable Smart Home Interface using Occipitalis sEMG Detection and Classification

Luu, Brian; Hansberger, Bradley; Chiu, Mandy; Shivappa, Vinay Kumar Karigar; George, Kiran

doi:10.1109/uemcon.2018.8796615

Cited by 6 publications

(2 citation statements)

References 14 publications

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“…auditory responses [44], and need reporting for disable persons [45]. Further, the Ultracortex Mark IV Headset is a headset designed to be 3D printed using a hobby-level 3D printer.…”

Section: Consumer-grade Eeg Headsetsmentioning

confidence: 99%

Design and Implementation of EEG-Mechatronic System Interface for Computational Intelligence

Aume¹,

Pal²,

Mukhopadhyay³

2022

Preprint

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The devices that can read Electroencephalography (EEG) signals have been widely used for Brain-Computer Interfaces (BCIs). Popularity in the field of BCIs has increased in recent years with the development of several consumergrade EEG devices that can detect human cognitive states in real-time and deliver feedback to enhance human performance. Several studies are conducted to understand the fundamentals and essential aspects of EEG in BCIs. However, the significant issue of how can consumer-grade EEG devices be used to control mechatronic systems effectively has been given less attention. In this paper, we have designed and implemented an EEG BCI system using the Open-BCI Cyton headset and a user interface running a game. We employ real-world participants to play a game to gather training data that was later put into multiple machine learning models, including a linear discriminant analysis (LDA), k-nearest neighbours (KNN), and a convolutional neural network (CNN). After training the machine learning models, a validation phase of the experiment took place where participants tried to play the same game but without direct control, utilising the outputs of the machine learning models to determine how the game moved. We find that a CNN trained to the specific user playing the game performed with the highest activation accuracy from the machine learning models tested, allowing for future implementation with a mechatronic system.

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“…auditory responses [44], and need reporting for disable persons [45]. Further, the Ultracortex Mark IV Headset is a headset designed to be 3D printed using a hobby-level 3D printer.…”

Section: Consumer-grade Eeg Headsetsmentioning

confidence: 99%

Design and Implementation of EEG-Mechatronic System Interface for Computational Intelligence

Aume¹,

Pal²,

Mukhopadhyay³

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…A prototype of SSVEP-based BCI for home appliances control is presented in [15]. Surface electromyography (sEMG) readings from the occipitalis region have been used to drive a home automation system [16]. The "Neurophone" [17] uses hidden Markov models trained to recognize mental instructions via the Gamma feature band.…”

Section: Introductionmentioning

confidence: 99%

Brain–Computer-Interface-Based Smart-Home Interface by Leveraging Motor Imagery Signals

Cariello,

Sanalitro,

Micali

et al. 2023

Inventions

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In this work, we propose a brain–computer-interface (BCI)-based smart-home interface which leverages motor imagery (MI) signals to operate home devices in real-time. The idea behind MI-BCI is that different types of MI activities will activate various brain regions. Therefore, after recording the user’s electroencephalogram (EEG) data, two approaches, i.e., Regularized Common Spatial Pattern (RCSP) and Linear Discriminant Analysis (LDA), analyze these data to classify users’ imagined tasks. In such a way, the user can perform the intended action. In the proposed framework, EEG signals were recorded by using the EMOTIV helmet and OpenVibe, a free and open-source platform that has been utilized for EEG signal feature extraction and classification. After being classified, such signals are then converted into control commands, and the open communication protocol for building automation KNX (“Konnex”) is proposed for the tasks’ execution, i.e., the regulation of two switching devices. The experimental results from the training and testing stages provide evidence of the effectiveness of the users’ intentions classification, which has subsequently been used to operate the proposed home automation system, allowing users to operate two light bulbs.

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An Interaction Framework for Designing Systems for Virtual Home Assistants and People with Dysarthria

Jaddoh,

Loizides,

Lee

et al. 2023

Univ Access Inf Soc

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People with speech impairments usually use assistive technology devices to assist them with communication and daily tasks. These devices can be controlled using different modalities, such as touch, eye gaze, gestures, and others. This article proposes a standardized methodology for designing non-verbal voice cue interactive systems that enable people with dysarthria to vocally interact with virtual home assistants (VHAs). We adopted a qualitative data-gathering approach to gain insights into users’ experiences and requirements and to determine crucial design elements for designing interactive voice assistants for people with dysarthria. Nineteen participants with varying levels of dysarthria took part in the study to create the framework. A system was then built using the proposed framework, and an additional test was performed with a further seven participants to validate the created system, thus inferring the validity of the framework. Our work empirically demonstrates how an informed, structured design of a fast, direct (verbal rather than forcing users to change modalities or using an intermediate device) method of communication improves the usability of VHAs for people with dysarthria while simultaneously allowing for a more authentic experience. The current data also highlighted that using non-verbal voice cues would be a convenient option. By providing a reproducible framework for developing non-verbal interactive systems for VHAs, we can increase the accessibility of said devices, their usability, and their user experience.

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Scalable Smart Home Interface using Occipitalis sEMG Detection and Classification

Cited by 6 publications

References 14 publications

Design and Implementation of EEG-Mechatronic System Interface for Computational Intelligence

Design and Implementation of EEG-Mechatronic System Interface for Computational Intelligence

Brain–Computer-Interface-Based Smart-Home Interface by Leveraging Motor Imagery Signals

An Interaction Framework for Designing Systems for Virtual Home Assistants and People with Dysarthria

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