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. 2016 Aug 26:10:416.
doi: 10.3389/fnhum.2016.00416. eCollection 2016.

Feasibility of BCI Control in a Realistic Smart Home Environment

Nataliya Kosmyna  1 , Franck Tarpin-Bernard  2 , Nicolas Bonnefond  3 , Bertrand Rivet  4
Affiliations

Feasibility of BCI Control in a Realistic Smart Home Environment

Nataliya Kosmyna et al. Front Hum Neurosci. .

Abstract

Smart homes have been an active area of research, however despite considerable investment, they are not yet a reality for end-users. Moreover, there are still accessibility challenges for the elderly or the disabled, two of the main potential targets for home automation. In this exploratory study we design a control mechanism for smart homes based on Brain Computer Interfaces (BCI) and apply it in the "Domus" smart home platform in order to evaluate the potential interest of users about BCIs at home. We enable users to control lighting, a TV set, a coffee machine and the shutters of the smart home. We evaluate the performance (accuracy, interaction time), usability and feasibility (USE questionnaire) on 12 healthy subjects and 2 disabled subjects. We find that healthy subjects achieve 77% task accuracy. However, disabled subjects achieved a better accuracy (81% compared to 77%).

Keywords: EEG; brain computer interfaces; conceptual imagery; smart home; usability.

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Figures

Figure 1
Figure 1
The “Domus” Smart Home.
Figure 2
Figure 2
An illustration of the functioning of the BCI system and the classifier.
Figure 3
Figure 3
The electrode placements for conceptual imagery used for the g.tec USBAmp. The dark gray electrode is the reference, the light gray ones are the CI electrodes.
Figure 4
Figure 4
Visual stimulations used for each concept for the conceptual BCI control. The cup toggles the kettle on or off, the lamp toggles the lights on or off, the blinds toggle the blinds to be raised or lowered and the TV triggers the TV on or off.
Figure 5
Figure 5
Timeline for the experiments. “Familiarization” phase let the users test the BCI without any actuation of the smart home appliances. The “Testing in smart home” phase is the same as familiarization, except the users had the actuation active.
Figure 6
Figure 6
Calibration of the toggle blinds class.
Figure 7
Figure 7
Averaged answers given by users about the naturalness of the action, the easiness of conceptualization and the adequateness to expectation on a scale from 1 to 7. The error bars are the 95% confidence interval.
Figure 8
Figure 8
A bar chart summarizing the median answers of the user for each question category across Experiment 1 and 2. The error bars are the 95% bootstrapping confidence intervals.
Figure 9
Figure 9
Bar charts summarizing the median task accuracy and time to activation across Experiments 1 and 2. The error bars are the 95% bootstrapping confidence intervals.
See this image and copyright information in PMC

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