2nd Reading May 2, 2020 16:12 2050018 International Journal of Neural Systems, Vol. 30, No. 7 (2020) 2050018 (15 pages) c The Author(s) DOI: 10.1142/S0129065720500185 A Prototype of EEG System for IoT Francisco Laport, Adriana Dapena∗,PaulaM.Castro, Francisco J. Vazquez-Araujo and Daniel Iglesia Department of Computer Engineering CITIC Research Center & University of A Coru˜na Campus de Elvi˜na, A Coru˜na 15071, Spain ∗[email protected] Accepted 5 February 2020 Published Online 4 May 2020 In this work, we develop open source hardware and software for eye state classification and integrate it with a protocol for the Internet of Things (IoT). We design and build the hardware using a reduced number of components and with a very low-cost. Moreover, we propose a method for the detection of open eyes (oE) and closed eyes (cE) states based on computing a power ratio between different frequency bands of the acquired signal. We compare several real- and complex-valued transformations combined with two decision strategies: a threshold-based method and a linear discriminant analysis. Simulation results show both classifier accuracies and their corresponding system delays. Keywords: Electroencephalography; Internet of Things; prototype; signal processing. 1. Introduction NeuroSky Mindwave.12 However, both the Emotiv or NeuroSky devices require the use of accompany- Brain–Computer Interfaces (BCI) are communica- ing proprietary software. tion systems that monitor the cerebral activity and In this work, we present a prototype of an EEG translate certain characteristics, corresponding to 1 device designed to acquire signals using a reduced user intentions, to commands for device control. Int. J. Neur. Syst. Downloaded from www.worldscientific.com number of sensors. The system is composed of low- One of the greatest challenges of BCI technology cost components, including a dual core microcon- is the development of wearable and portable elec- troller that allows us to perform all operations asso- trode devices that are minimally invasive and ready- ciated to signal classification simultaneously to the to-use with short training times.2,3 In particular, transmission of data to the Internet of Things (IoT) current research is focused on the potential of Elec- environment. Our first objective is the assessment of troencephalography (EEG)4–6 techniques to capture by 213.60.74.193 on 06/01/20. Re-use and distribution is strictly not permitted, except for Open Access articles. its performance in determining the user’s eye states, the brain activity associated to user intent.7–9 This i.e, open eyes (oE) or closed eyes (cE), using differ- requires the design and development of friendly ent strategies based on short training times. These devices with real-time operation and adaptation for strategies are as follows: people with motor functional diversity. Recently, dif- ferent companies and studies have presented com- (1) Real-valued instead of complex-valued trans- mercial products with the aim of opening the EEG forms, since, in general, the use of complex- technology to applications not restricted to medical 2,10 11 valued signals requires more than twice the num- diagnosis, such as the Emotiv Epoc and the ber of computational operations. ∗ Corresponding author. This is an Open Access article published by World Scientific Publishing Company. It is distributed under the terms of the Creative Commons Attribution 4.0 (CC BY) License which permits use, distribution and reproduction in any medium, provided the original work is properly cited. 2050018-1 2nd Reading May 2, 2020 16:12 2050018 F. Laport et al. (2) Sliding windows with overlapping instead of the caretakers. In a more recent study with the same traditional approach based on nonoverlapped headset, Narayana et al.19 present a BCI running on windows, thus reducing delays between acquisi- an Android phone which locks/unlocks a wheelchair tion and decision. and controls its movements through an IoT environ- (3) A classifier based either on a threshold-based ment. Lee et al.20 present a BCI-controlled mobile method or on Linear Discriminant Analysis robot for telepresence which is developed in an IoT (LDA). system and used for the communication between sub- (4) A correction system for the mitigation of decision ject and robot. errors. In previous work, we have studied the control of IoT devices using the detection of cE and oE Our second objective is the use of the acquired infor- states.21 For the detection of each state, we ana- mation about the user’s eye state for the control of lyzed the alpha and beta waves of the EEG signal. IoT devices by means of ON/OFF operations. Alpha waves, whose frequency ranges from 8 Hz to This paper is organized as follows. Section 2 sum- 13 Hz, are associated with the most relaxed and sta- marizes some of the most important previous works ble brain state; whereas beta waves, in the frequency related to the utilization of EEG for IoT and to the rangefrom13Hzto22Hz,areassociatedwithstates classification of cE and oE states from acquired sig- of high wakefulness.22–25 nals. Section 3 describes our open source device for Both the eye state identification and the eye- EEG signal acquisition and an open solution from gaze analysis have become an active research field OpenBCI.13 Section 4 shows the architecture used during the last years due to their implication in to integrate the EEG device with IoT elements. Sec- human–machine interfaces.26,27 In particular, EEG tion 5 explains the proposed method for cE and oE eye state detection has been successfully applied in classification. The methodology and materials used a wide variety of domains,28 such as driving drowsi- for the experiments are presented in Sec. 6. Section 7 ness detection,29 infant sleep-waking state classifi- shows the results obtained from a set of test individ- cation30 and stress features identification,31 among uals. Section 8 analyzes these results. Finally, Sec. 9 others. Several papers have associated cE and oE contains the more relevant conclusions of this work. with particular frequency and amplitude ranges of the brain waves.22–25 For instance, Kirkup et al.32 2. Related Work present a home automation control system for a rapid The IoT paradigm refers to a global dynamic infras- on/off switch appliance which filters the EEG signal Int. J. Neur. Syst. Downloaded from www.worldscientific.com tructure based on interoperable communication pro- between 8 Hz and 12 Hz and employs the resulting tocols that integrate physical and virtual objects alpha values to determine the user’s eye state based into an information network.14 Among other applica- on a threshold. tions, home automation or smart homes are rapidly In a more recent study, Saghafi et al. propose gaining interest.15,16 The aim of such systems is to the use of delta and theta bands for these purposes, make the home environment not only comfortable applying an 8 Hz low-pass filter to the EEG signal33 by 213.60.74.193 on 06/01/20. Re-use and distribution is strictly not permitted, except for Open Access articles. and accessible, but also to optimize and automate and using different methods for the extraction of the the use of appliances like TV sets, air conditioners, principal features in eye state classifiers such as, for light bulbs, ovens or washing machines. example, Multivariate Empirical Mode Decomposi- The development of low-cost EEG devices as tion (MEMD), Logistic Regression (LR), Artificial a tool for IoT has been presented in some recent Neural Networks (ANN) or Support Vector Machine papers.17 For instance, Jadadish et al. present a (SVM). Their proposed algorithm detected the eye BCI application which detects voluntary blinks of state with an accuracy of 88.2% in less than 2 s. the user and controls electrical appliances included On the other hand, Naderi et al.34 propose a in an IoT system. Mathe et al.18 connect a BCI technique based on Welch’s method for estimating application which employs the Mindwave12 consumer the Power Spectral Density (PSD) and on Recurrent EEG device to estimate the user’s depression level Neural Networks (RNNs) to differentiate a relaxed with an IoT ecosystem responsible for notifying the and open eye state from an epileptic seizure. Features 2050018-2 2nd Reading May 2, 2020 16:12 2050018 A Prototype of EEG System for IoT from EEG time series and PSD levels were extracted 3. Open Devices to train and test the classifier which, at the end, The elements of the EEG device we developed are exhibited an accuracy of 100%. In another study on shown in Fig. 1. This prototype has a total of three the same dataset, Acharya et al.35 propose the use of sensors: input, reference and ground. Convolutional Neural Networks (CNN) for the devel- The input signal is amplified and bandpass fil- opment of a Computer-Aided-Diagnosis (CAD) sys- tered between 4.7–29.2 Hz. We use an AD8221 instru- tem that automatically detects seizure using EEG mentation amplifier followed by: (i) a 50 Hz notch signals. They implement an algorithm based on a filter to avoid the interference of electric devices in 13-layer deep convolutional neural network for the the vicinity of the sensor wires; (ii) a second-order detection of normal, preictal, and seizure classes, low-pass filter; (iii) a second-order high-pass filter; where the normal state corresponds to a relaxed and (iv) a final bandpass filter with adjustable gain. and CE situation. Their proposed technique achieves The different filter stages that follow the instrumen- . an accuracy, specificity, and sensitivity of 88 67%, tation amplifier are shown in Fig.