Browsing by Author "Seneviratne, J.A."

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Feasibility of classifying brainwave data extracted from commercially available EEG headset using deep learning techniques
(Faculty of Science, University of Kelaniya, Sri Lanka, 2020) Gunawardhana, M.P.A.V.,; Jayatissa, C.A.N.W.K.; Seneviratne, J.A.
Electroencephalography (EEG) is the process of observing the electrical activity of the brain. In recent years there has been an increase in the availability of low-cost EEG headsets in the consumer market. This study was conducted using such a device, the Emotiv Insight 5-channel EEG headset. The objective of this study is to visually stimulate the brain and successfully identify the stimulus by classifying the EEG data using deep neural network techniques. Since the used EEG headset only contains 5 electrodes, it is quite difficult to classify the signals without employing learning-based algorithms. By nature, the human brain unlikely to stay idle for a long period. Due to that, the collection of EEG recordings must be done carefully without contaminating the data. To achieve this the proposed method of data collection is done through the help of a Graphical User Interface (GUI) which was programmed using the Python language. The GUI automates the tasks of recording, saving, and managing the EEG data. First, the subject was placed in front of a screen, in a quiet environment and the EEG headset was put on. After the recording begins, the GUI randomly chooses an image from an image-set which was provided beforehand and display it on the screen for 2 seconds while recording the EEG data in the background. After 30 seconds, the recording stops automatically, and the captured data is saved with the necessary information. The above periods were chosen specifically to limit the stress of watching a sequence of images for a long time period. The subject was informed about what types of image classes are shown and instructed to “identify” the class of the image. For the following analysis, 200 recordings of 30-second records from one subject were used. They were recorded using images of “Cats” and “Dogs”. The initial results of this study were obtained by employing two data classification methods. The first analysis is done with a 1-Dimensional Convolutional Neural Network (1D-CNN) and it achieved an accuracy of 52%. The second method employed a spectrogram based 2-Dimensional Convolutional Neural Network (2D-CNN) with an accuracy of 54%.
Smart System Using Lora Technology to Connect Rural Areas Underserved By Existing Internet and Telecommunication Technologies
(The Electrochemical Society, 2022) Jayasekaraa, L.D.P.S.; Gurusinghe, T.N.; Wijesooriya, H.E.; Seneviratne, J.A.; Ranaweera, A.L.A.K.; Jayathilaka, K.M.D.C.; Wijesundera, L.B.D.R.P.; Kalingamudali, S.R.D.
LoRa, Sigfox, and Narrowband-Internet of Things (NB-IoT) are some of the long-distance, low-power wireless communication technologies developed in the recent past. The proposed system consists of mainly nodes and a gateway as the fundamental system architecture. Nodes only communicate with the gateway individually and the gateway communicates with all the nodes separately and wirelessly. System in this proposed study, uses long range low power RF wireless communication technique for primary data communication, where an Internet connection will not be required for the communication between the gateway and the nodes. Any number of nodes can be paired with the gateway, and the gateway can individually communicate with each and every node. Furthermore, gateways have the ability of storing real-time data. Due to its unique design, the proposed system in this study, can achieve addressable, bidirectional, and continuous data communication even without the Internet connection. The bidirectional communication design of this proposed system facilitates real time and uninterrupted simultaneous handling of monitoring/sensor devices and controller devices without the need of a separate controlling system. As this system consists of those unique features, it is recommended to use in the rural areas underserved by current internet and telecommunication technologies. Furthermore, with the in-built option to get connected to the Internet, this system can be further expanded to an IoT based addressable data communication, processing, and visualization systems by eliminating the major technical problems in typical IoT systems such as interrupted communication and data losses during an Internet connection failure, power concerns and customization issues. This system is highly customizable, and the nodes and the connected devices can be controlled through the gateway or remote dashboard by assigning automated or user defined custom commands. These features together improve the robustness of the system and facilitates enhanced data recovery in case of a failure in the Internet connectivity.