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Singapore

Organization for Human Brain Mapping (OHBM) 2018

Singapore

Organization for Human Brain Mapping (OHBM) 2018

Hawaii, USA

Asia-Pacific Signal and Information Processing Association (APSIPA) 2018.

Hawaii, USA

Asia-Pacific Signal and Information Processing Association (APSIPA) 2018.

Shanghai, China

It was exciting to be off on a journey # 16 hr Layover.

Shanghai, China

It was exciting to be off on a journey # 16 hr Layover.

California, USA

Asilomar Conference on Signals, Systems and Computers (ACSSC) 2019.

California, USA

Asilomar Conference on Signals, Systems and Computers (ACSSC) 2019.

AMITA GIRI

Curriculum Vitae

Amita Giri

Amita Giri defended her Ph.D. degree in the Department of Electrical Engineering at Indian Institute of Technology (IIT) Delhi, India, in March 2022. She received her bachelors degree in Electronics & Communication Engineering from National Institute of Technology (NIT) Uttarakhand, India in July 2017, where neuroscience captured her interest before she knew such a term existed. Her passion for research and curiosity to learn more about complex human brain led her to enroll in a Ph.D. direct after her undergraduation. She is a recipient of the prestigious Prime Minister's Research Fellowship (PMRF), awarded to the country's excellent doctoral candidates for pursuing their research.

Her research interests include Biomedical signal processing, Brain soucre localization, Computational neuroscience, Brain computer interface (BCI) and Machine learning.

Lalan Kumar

(Advisor)

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Tapan K. Gandhi

(Co-Advisor)

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EDUCATION

Amita Giri

  • Ph.D. (July 2017 - Mar 2022)
    • Indian Institute of Technology , Delhi

      Department of Electrical Engineering
      Fellowship : Prime Minister's Research Fellowship


    Amita Giri
  • B.Tech (2013 - 2017)
    • National Institute of Technology , Uttarakhand
      Department of Electronics & Communication Engineering
      Fellowship : L'Oreal India For Young Women in Science (2013 - 2017)
      Uttarakhand State Government Scholarship (2013 - 2017).

    RESEARCH

    ResearchGate , Google Scholar , Linkedin

    My current research interests include:
    • Biomedical Signal Processing
    • Brain source localization
    • Computational Neuroscience
    • Brain Computer Interface (BCI)
    • Machine Learning & Deep Learning

    1. Brain Source Localization in Head Harmonics Domain
    2. A. Giri research focuses on the low computational cost of active brain source localization to prevent delay in the diagnosis of epileptic seizure location. The localization performance is limited by the head shape assumption, as the EEG data is spatially sampled over the head for efficient data representation. In literature, the human head is approximated by spherical shape. Hence, spherical harmonics have been the natural choice for EEG source reconstruction and localization. She developed a set of basis functions called Head Harmonics, which improves the quality of non-invasive source localization when compared to spherical or spatial domain processing. Her future research investigates the applicability of developed harmonics under realistic head modeling scenario. She is also interested in developing a head harmonics based adaptive EEG acquisition system prototype.

    3. Anatomical Surface Reconstruction using Hemispherical Harmonics
    4. An accurate representation of a three-dimensional (3D) geometry is crucial for structural analysis in many biomedical applications. In medical shape analysis, there exists a wide range of hemisphere-like anatomical structures such as brain, skull and scalp, which are naturally parameterized using the upper hemisphere only. The representation of such hemispherical objects using basis functions defined over the full spherical domain introduces discontinuities at the boundary of the hemisphere and requires a large number of coefficients. Therefore, the two hemispherical area-preserving parameterization methods for simply-connected open and closed surfaces is developed. The hemispherical harmonics basis functions are therfore utilized to yield an accurate representation of hemisphere-like anatomical surfaces. In near future work, she is interested in doing the adaptive parametrization (not constrainede to be on hemisphere or sphere) of input surfaces and use corresponding harmonics for shape description.

    5. Cortical Source Domain based Brain Computer Interface (BCI)
    6. In BCI, decoding the motor task from non-invasive EEG measurements is a challenging problem. It is due to the fact that encoding is assumed to be deep within the brain and is not easily accessible by the scalp recordings. The ability to know the source generators of the intended motor task from EEG may lead to huge improvements in BCI by providing a continuous task relevant neural signals. To overcome these issues and to study the brain activity on the motor cortex, cortical source domain processing is proposed. Her finding emphasize to use the spatial source distribution knowledge in neuro feedback training of BCI systems. She is also interested on enhancing the user's control abilities, as the performance of any BCI system relies heavily on a user's attention level and ability to modulate sensory motor rhythms. Since, considerable progress has been made in "computer" side, while little work has been done on "brain" perspective. She believes her work opens a wide range of possiblities to patients suffering from neuromuscular disabilities, stress and anxiety.

    7. Kinematic Trajectory Prediction from Brain Signals
    8. The ability to reconstruct the kinematic parameters of hand movement using non-invasive electroencephalography (EEG) is essential for external device control. For system development, the conventional classification based brain computer interface (BCI) controls external devices by providing discrete control signals to the actuator. A continuous kinematic reconstruction from EEG signal is better suited for practical BCI applications. The state-of-the-art multi-variable linear regression (mLR) method provides a continuous estimate of hand kinematics, achieving maximum correlation of upto 0.67 between the measured and the estimated hand trajectory. Three novel source aware deep learning models multi layer perceptron (MLP), convolutional neural network - long short term memory (CNN-LSTM), and wavelet packet decomposition (WPD) CNN-LSTM are proposed for motion trajectory prediction (MTP). Our methods provide statistically significant improvement in kinematic trajectory from EEG signals compared to existing state-of-the-art mLR method. Our work bridges the gap between the control and the actuator block, enabling real time BCI implementation.

    PUBLICATIONS

  • Journal
  • [J6]. A. Giri, L. Kumar, N. Kurwale and T. Gandhi,"Anatomical Harmonics Basis based Brain Source Localization with Application to Epilepsy ", 2021, Under Review.

    [J5]. G. P. T. Choi, A. Giri and L. Kumar, "Adaptive area-preserving parameterization of open and closed anatomical surfaces " arXiv preprint arXiv:2111.04265, 2021, Under Review.

    [J4]. S. Pancholi, A. Giri, A. Jain, L. Kumar and S. Roy, "Source Aware Deep Learning Framework for Hand Kinematic Reconstruction using EEG Signal " in IEEE Transactions on Cybernetics, 2022 (Accepted).

    [J3]. A. Giri, L. Kumar and T. Gandhi, "Cortical Source Domain Based Framework For Enhanced Brain Computer Interface Applications in IEEE Sensors Letters" , vol. 5, No. 12, 2021.

    [J2]. A. Giri, L. Kumar and T. Gandhi, "Brain Source Localization in Head Harmonics Domain " in IEEE Transactions on Instrumentation and Measurement , vol. 70, pp. 1-10, 2020.

    [J1]. A. Giri, G.P.T. Choi and L. Kumar, "Open and Closed Anatomical Surface Description via Hemispherical Area-Preserving Map" in Signal Processing , Elsevier, vol. 180, 107867, 2020.

  • Conference
  • [C4]. A. Giri, L. Kumar and T. Gandhi, "Robust EEG Source Localization Using Subspace Principal Vector Projection Technique" in 28th European Signal Processing Conference (EUSIPCO) , pp. 1075-1079, Amsterdam, Netherlands, IEEE, 2020.

    [C3]. A. Giri, L. Kumar and T. Gandhi, "Head Harmonics based EEG Dipole Source Localization" in 53rd Asilomar Conference on Signals, Systems and Computers (ACSSC) , pp. 2149-2153, Pacific Grove, CA, USA, 2019, IEEE.

    [C2]. A. Giri, L. Kumar and T. Gandhi, "EEG Dipole Source Localization in Hemispherical Harmonics Domain" in 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) , pp. 679-684, Honolulu, HI, USA, 2018, IEEE.

    [C1]. S.K. Wupadrasta, A. Giri, L. Kumar and T. Gandhi, "Hemispherical Harmonics based Brain Source Localization" in 2018 Organization for Human Brain Mapping (OHBM), Singapore.

    TEACHING

    Teaching Assistant in the Department of Electrical Engineering at IIT Delhi
    • ELL 824 Selected Topics in Information Processing II, Semester I 2020 - 21
    • ELL 319 Digital Signal Processing , Semester II 2019 - 20
    • ELL 100 Basic Electrical Engineering, Semester I 2019 - 20
    • ELL 319 Digital Signal Processing , Semester II 2018 - 19
    • ELL 319 Digital Signal Processing , Semester I 2018 - 19
    • ELL 319 Digital Signal Processing , Semester I 2017 - 18
    Mentor of M.Tech students research project
    • Boundary Element Forward Data model, semester I 2019-20
    • Study mathematical equations of forward model., semester I 2018-19
    Mentor of B.Tech students research project
    • Deep Learning Model for Motor Imagery Brain Computer Interface, Semester II 2019-20
    • Dipole Source Localization, Semester II 2018-19
    Mentor of B.Tech students summer project
    • Interference suppression in EEG source Analysis, Summer Break 2019
    • Comparative analysis of various brain source localization method, Summer Break 2018

    CONTACT

    Amita Giri

    Ph.D. Research Scholar

    Multichannel Signal Processing Lab
               MS 201

    Department of Electrical Engineering
               Indian Institute of Technology, Delhi

    Email: Amita.Giri@ee.iitd.ac.in

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    Last Updated

    28-03-2022

    NEWS

    • April 2022 : I was invited as the event speaker at the Brain Mapping and Artificial Intelligence Workshop 2022. I had presented my work on "Brain Source Localization Based BCI Application"..
    • Mar 2022 : Our paper entiteld "Source Aware Deep Learning Framework for Hand Kinematic Reconstruction using EEG Signal" has been accepted for publication in IEEE Transactions on Cybernetics.
    • Mar 2022 : I have successfully defended my thesis entiteld "Spatial and Anatomical Harmonics Domain based Brain Source Localization".
    • Feb 2022 : Several online articles and newspaper clipping in press about me getting a Post-Doc position at MIT, USA.
    • Dec 2021 : Our paper entitled "Subspace Principal Vector Projection Technique based Interference Suppression for BCI Application" received BEST PAPER AWARD in the International Conference on Advances in Communication & Computing Technology (ICACCT-2021).
    • Aug 2021 : [1],[2],[3] and [4] You tube interview about my research.

    EXTRA-CURRICULAR ACTIVITIES

    Amita Giri

    • Badminton
    Amita Giri


    • Cricket




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