Brain-Computer Interface, Internet of Things, Machine/Deep learning, FPGA/ASIC Design
11
Scopus Publications
Scopus Publications
A Novel Experimental Study to Enhance the Attentional State using EEG Signals Jagadish Bandaru, Rajalakshmi Pachumutthu 2020 IEEE Sensors Applications Symposium Sas 2020 Proceedings, 2020 In this paper, we propose a simple low-complex classification framework for the cognitive enhancement with the sustained attention stimuli using Electroencephalography (EEG) signals. The visual stimuli comprise of four face images: two happy (one male and one female) and two unhappy (one male and one female). The neuronal response is decoded using a combination of discrete wavelet transform (DWT) and ensemble classifier. The features are extracted by decomposition of recorded EEG signals using Daubechies wavelet filter (db4) and used the statistical methods such as the absolute mean value, power, and standard deviation for classification. The proposed methodology is validated on in-house recorded visual attention EEG (VA-EEG) dataset using six subjects (three males, three females) and evaluated the performance on six binary combinations of facial stimuli. The performance results show that the binary combination of male happy (MH) and female happy (FH) facial stimuli aids in cognitive enhancement for the people suffering from cognitive symptoms. The proposed low-complex feature extraction classification framework obtained a mean classification accuracy (CA) and a mean kappa value of 86.58% and 0.72, respectively.
A real-time health 4.0 framework with novel feature extraction and classification for brain-controlled iot-enabled environments B. Jagadish, P. K. Mishra, M. P. R. S. Kiran, P. Rajalakshmi Neural Computation, 2019 In this letter, we propose two novel methods for four-class motor imagery (MI) classification using electroencephalography (EEG). Also, we developed a real-time health 4.0 (H4.0) architecture for brain-controlled internet of things (IoT) enabled environments (BCE), which uses the classified MI task to assist disabled persons in controlling IoT-enabled environments such as lighting and heating, ventilation, and air-conditioning (HVAC). The first method for classification involves a simple and low-complex classification framework using a combination of regularized Riemannian mean (RRM) and linear SVM. Although this method performs better compared to state-of-the-art techniques, it still suffers from a nonnegligible misclassification rate. Hence, to overcome this, the second method offers a persistent decision engine (PDE) for the MI classification, which improves classification accuracy (CA) significantly. The proposed methods are validated using an in-house recorded four-class MI data set (data set I, collected over 14 subjects), and a four-class MI data set 2a of BCI competition IV (data set II, collected over 9 subjects). The proposed RRM architecture obtained average CAs of 74.30% and 67.60% when validated using datasets I and II, respectively. When analyzed along with the proposed PDE classification framework, an average CA of 92.25% on 12 subjects of data set I and 82.54% on 7 subjects of data set II is obtained. The results show that the PDE algorithm is more reliable for the classification of four-class MI and is also feasible for BCE applications. The proposed low-complex BCE architecture is implemented in real time using Raspberry Pi 3 Model B+ along with the Virgo EEG data acquisition system. The hardware implementation results show that the proposed system architecture is well suited for body-wearable devices in the scenario of Health 4.0. We strongly feel that this study can aid in driving the future scope of BCE research.
A Novel Feature Extraction Framework for Four Class Motor Imagery Classification using Log Determinant Regularized Riemannian Manifold B. Jagadish, P. Rajalakshmi Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society EMBS, 2019 Brain-Computer Interface (BCI) systems allow the person in communicating with the external world using Electroencephalography (EEG). Motor Imagery (MI) based BCI systems play a vital role in interacting with the external environment. In this paper, we propose a novel robust feature extraction and classification framework for four class MI classification to improve the classification accuracy. The proposed architecture is developed using log-determinant (log-det) based Regularized Riemannian mean (LDRRM) and linear SVM. The robustness of features extracted from the four class MI data is improved to the outliers and noise by using the proposed LDRRM framework. We evaluated the performance of the proposed LDRRM classification framework on publicly available four class MI dataset 2a of BCI competition IV. The performance results show that the proposed LDRRM classification architecture obtained a mean classification accuracy of 69.12%, also achieved 1.54% higher classification accuracy when compared with the existing studies.
A residual phase noise compensation method for IEEE 802.15.4 compliant dual-mode receiver for diverse low power IoT applications Abdullah Zubair Mohammed, Ajay Kumar Nain, Jagadish Bandaru, Ajay Kumar, D. Santhosh Reddy, et al. IEEE Internet of Things Journal, 2019 The Internet of Things (IoT) with its plethora of applications brings up new challenges in optimizing power consumption, error performance, latency, and throughput in its communication devices. Recently, there has been increasing necessity of reconfigurable and multistandard transceivers in order to bring adaptability in the IoT devices to suit the nature of the application and satisfy the aforementioned performance metrics as well. In this paper, we focus on the efficient design of receivers compliant with IEEE 802.15.4 which is a prominent protocol for low power IoT applications. We propose a robust phase noise compensation method which efficiently removes the residual phase noise remaining after the coarse frequency offset compensation due to direct-sequence spread spectrum operation on large packets. We also propose a dual-mode receiver using the proposed compensation method and showcase its ability to cater to the diverse nature of IoT applications. The detailed architecture of proposed dual-mode receiver is presented along with its FPGA prototyping and ASIC implementation. We have analyzed overall power consumption by the proposed dual-mode receiver considering the packet error rate and retransmission scenario. The results show that the proposed receiver saves significant energy consumption by changing its mode in favorable channel environments.
A novel classification for EEG based four class motor imagery using kullback-leibler regularized riemannian manifold P. K. Mishra, B. Jagadish, M. P. R. S. Kiran, P. Rajalakshmi, D Santhosh Reddy 2018 IEEE 20th International Conference on E Health Networking Applications and Services Healthcom 2018, 2018 Recent advances in the Brain-Computer Interface (BCI) systems state that the accurate Motor Imagery (MI) classification using Electroencephalogram (EEG) plays a vital role. In this paper, we propose a novel real-time feature extraction and classification architecture for four class MI using a combination of Kullback-Leibler Regularized Riemannian Mean (KLRRM) and Linear SVM. By using the KL regularization, the robustness of the features extracted to the noise and outliers is improved. The performance of the proposed architecture is analyzed on the four class MI dataset 2a from the BCI Competition IV. The performance analysis shows that the proposed architecture achieves an average classification accuracy of 74.43% and 51.53% for both the good and noisy subjects respectively. Also, the emphasis is laid on understanding the performance of regularization, and the improvement of robustness to the noise and outliers is demonstrated using the noisy subjects.
A novel system architecture for brain controlled IoT enabled environments B. Jagadish, M. P. R. S. Kiran, P. Rajalakshmi 2017 IEEE 19th International Conference on E Health Networking Applications and Services Healthcom 2017, 2017 Brain Computer Interface (BCI) has recently gained much popularity due to plethora of its applications. In this paper, we propose a novel system architecture to utilize brain signals for controlling Internet of Things enabled environments. The proposed architecture aids in translating brain signals to commands that interact with or control the environment using IoT actuation networks thus executing user desired actions. It comprises of novel, low complex and low power intelligent signal processing architecture for detection of voluntary eye blinks by isolating involuntary eye blinks and IoT enabled wireless actuation network for controlling the environment using commands generated from EEG signal. For the real time performance analysis of the proposed architecture, we developed a wearable device which acquires dual channel EEG using electrodes at Fp1 and Fp2 locations. From the acquired EEG data, the device detects the voluntary eye blinks of the patient and use this information in controlling the environment such as switching HVAC system, lighting or electric fan etc. Performance analysis shows that the proposed intelligent signal processing architecture detects the voluntary eye blinks with 95.2% accuracy when tested on 10 subjects with a low power consumption of 165 mW.
A Secure Phase-Encrypted IEEE 802.15.4 Transceiver Design Ajay Kumar Nain, Jagadish Bandaru, Mohammed Abdullah Zubair, Rajalakshmi Pachamuthu IEEE Transactions on Computers, 2017 With the proliferation of Internet of Things (IoT), the IEEE 802.15.4 physical layer is becoming increasingly popular due to its low power consumption. However, secure data communication over the network is a challenging issue because vulnerabilities in the existing security primitives lead to several attacks. The mitigation of these attacks separately adds significant computing burden on the legitimate node. In this paper, we propose a secure IEEE 802.15.4 transceiver design that mitigates multiple attacks simultaneously by using a physical layer encryption approach that reduces the computations at the upper layers. In addition to providing confidentiality and integrity services, the proposed transceiver provides sufficient complexity to various attacks, such as cryptanalysis and traffic analysis attacks. It also significantly improves the lifetime of the node in the presence of a ghost attacker by preventing the legitimate node from processing the bogus messages and hence combats against energy depletion attacks. The simulation results show that a high symbol error rate at the adversary can be achieved using the proposed transceiver without affecting the throughput at the legitimate node. In this paper, we also analyze the hardware complexity by developing an FPGA and ASIC prototype of the proposed transceiver.
Reconfigurable dual mode IEEE 802.15.4 digital baseband receiver for diverse IoT applications Mohammed Abdullah Zubair, Ajay Kumar Nain, Jagadish Bandaru, P. Rajalakshmi, U.B. Desai 2016 IEEE 3rd World Forum on Internet of Things Wf Iot 2016, 2016 IEEE 802.15.4 takes a center stage in IoT as Low-Rate Wireless Personal Area Networks(LR-WPANs). The standard specifies Offset Quadrature Phase Shift Keying Physical Layer (O-QPSK PHY) with half-sine pulse shaping which can be either categorized under the class of M-ary PSK signals (QPSK signal with offset) or as Minimum Shift Keying (MSK) signal. M-ary PSK demodulation requires perfect carrier synchronization and has minimal error. MSK signals which falls under Continuous Phase Frequency Shift Keying can be demodulated non-coherently but error performance is not as good. In our paper, this dual nature of IEEE 802.15.4 PHY is exploited to propose a dual mode receiver comprising of QPSK demodulator chain and MSK demodulator chain as a single system on chip. The mode can be configured manually depending on the type of application or based on feedback from a Signal to Noise (SNR) indicator employed in the proposed receiver. M-ary PSK chain is selected for lower SNRs and MSK for higher SNRs. Each of these properties are analyzed in detail for both demodulator chains and we go on to prove that MSK detection can be used for low power, low complex and low latency while QPSK detection is employed for minimal error.
IoT enabled communication device with mixer less low complex QPSK based transmitter architecture for low frequency applications M. P. R. Sai Kiran, P. Rajalakshmi, B. Jagadish International Symposium on Wireless Personal Multimedia Communications Wpmc, 2015 Technological development in the area of wireless communications lead to the requirement of tight integration of both the digital and analog functional units. Integrating mixers is a challenging task, especially in mixed signal design. IoT communication devices require low design complexity as we expect millions of devices connected. In this paper we propose a mixer less low complex QPSK based transmitter architecture targeting low frequency applications which reduced the complexity in transmitter design. A prototype has been developed using Bipolar Junction Transistors (BJTs) and FPGA as the base band controller. The design can easily be adapted to MOSFET technology and modulation is achieved without the need of generating the carrier externally. The prototype developed was tested successfully by generating frequencies of range varying from 1 KHz to 120 MHz. The proposed architecture can also be used for any other digital modulation scheme such as BPSK, FSK etc.
Performance analysis of hybrid multiple radio IoT architecture for ubiquitous connectivity Y. SivaKrishna, P. Rajalakshmi, Jagadish Bandaru, Ajay Kumar, M. P. R. Sai Kiran, et al. IEEE World Forum on Internet of Things Wf Iot 2015 Proceedings, 2015 In this study, we propose a novel physical layer architecture which is a hybrid of IEEE 802.15.4 and IEEE 802.11b for providing ubiquitous connectivity in IoT remote sensing platforms. The proposed architecture multiplexes the commonly available functional units of IEEE 802.15.4 and IEEE 802.11b, thereby achieving significant area and power savings. In order to achieve this, we modified the existing PHY layer to incorporate QPSK and RC pulse shaping for both the radios during the process of modulation. Performance analysis shows that the proposed architecture resulted in an area savings of 17.2 % and power savings of 17.8 % compared to traditional architecture without any performance degradation. In addition the CMOS transistors count is reduced by 6592, which is a significant reduction in complexity when compared to traditional independent radio's architectures.
An on-chip robust real-time automated non-invasive cardiac remote health monitoring methodology Computing in Cardiology, 2014
