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Overview

Bidirectional Brain Computer Interfaces

Brain-computer interfaces (BCI) aim to treat disability due to loss of neural function, such as blindness and paralysis, by interfacing with the remaining functional neural circuit. Recent advances in high-resolution and large-scale implantable hardware devices have now enabled us to investigate large neural populations at cellular resolution, resulting in a wealth of basic neuroscientific data across brain regions, species, and tasks.

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Our goal is to build a computational toolbox for reading-out and writing-in into neural circuits, in real-time and at cellular-resolution. 

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Some questions we want to answer:

  1. How are flexible, high-degrees of freedom movements such as finger movements, limb movements and speech represented in the brain?

  2. Can we use intracortical array recordings in human participants with paralysis to enable full-body movement in virtual reality? 

  3. How do we reproduce rich spatio-temporal natural activity patterns using electrical stimulation?

  4. How do we use existing experimental data to develop the next-generation of low-power, large-scale and high-density implantable BCIs?

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We aim to make progress towards these questions by collaborating with hardware groups, basic neuroscientists and clinicians, both at the Rice University and the Texas Medical Center.

Intracortical BCI for Flexible Typing using Attempted Finger Movements
02:35

Intracortical BCI for Flexible Typing using Attempted Finger Movements

A BrainGate2 participant with two Utah arrays in motor cortex controlled virtual fingers to select characters on a custom-designed keyboard (Shah el., bioRxiv 2024).
Intracortical BCI for Virtual Drone Control using Attempted Finger Movements
02:55

Intracortical BCI for Virtual Drone Control using Attempted Finger Movements

A BrainGate2 participant controlled virtual finger movements to fly quadcopter in simulation (Willsey, Shah, et al., bioRxiv 2024).
Writing-in Neural Activity for Retinal Prosthesis
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Writing-in Neural Activity for Retinal Prosthesis

Temporal dithering a large number of simple, single-electrode electrical stimuli reproduces population activity in an ex-vivo lab-prototype of a cellular-resolution artificial retina (Shah et al., 2019, Shah*, Phillips* et al., 2024).
Individual Variability Across Neural Population Recordings
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Individual Variability Across Neural Population Recordings

A shared neural network was modulated by a low dimensional manifold to match responses across 95 ex-vivo MEA recordings of primate retina (Shah et al., 2022)

Team

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Nishal P. Shah

PI

Nishal is an Assistant Professor in the Electrical and Computer Engineering department at Rice, a core member of the Rice Neuroengineering Initiative, and a McNair Medical Scholar.

 

Previously, Nishal was the Milton Safenowitz postdoctoral researcher at the Neural Prosthetics Translational Lab at Stanford and a member of the BrainGate2 consortium. Working with participants with paralysis who are surgically implanted with microelectrode arrays, he has developed methods for decoding high degrees-of-freedom finger movements from the motor cortex and enabled flexible brain-computer interfaces for communication.

 

Nishal finished his PhD in Electrical Engineering from Stanford University in 2020, where he developed computational approaches to electrically stimulate the cells in the retina to restore vision. 

 

Previously, he finished his Bachelor's and Master's in Electrical Engineering from the Indian Institute of Technology, Delhi.

News

Join us!

We are actively seeking talented postdocs and graduate students to join our research team. If you're interested, please email us: bhaishahster[at]gmail.com.
 

Postdoctoral Scholars
 

We are looking for 1-3 postdoctoral scholars to lead research in high-degrees of freedom motor decoding, encompassing whole-body movement and speech. The specific project will be determined based on mutual interests. Potential options include:
 

  • Setting up a BCI rig and collaborating with clinical partners at the Texas Medical Center to initiate a human BCI trial.

  • Real-time, closed-loop decoding of speech and whole-body movements in virtual reality.

  • Investigating how the human brain composes simple movements into complex simultaneous and rapid sequential movements.

  • Identifying the neural representation and dynamics in multiple brain regions to understand how high-level thoughts are transformed into low-level movements.

  • Perturbing neural activity using precise, cellular-resolution electrical stimulation.
     

Postdoctoral scholars will play a crucial role in shaping our scientific direction and fostering a rigorous and collaborative lab culture.
 

Ideal candidates will have:

  • A strong quantitative background.

  • A PhD in Electrical Engineering, Computer Science, Mechanical Engineering, Neuroscience, or Statistics.

     

Research Engineer

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We are seeking a research engineer to build and maintain a BCI system for collecting experimental data from human participants with intra-cortical neural implants. This role is central to our human BCI projects and involves real-time neural signal processing, task visualization, and neural decoding. The research engineer will also help develop and maintain other technical infrastructure, such as robotic hands or digital interfaces.

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Research engineers will:

  • Be co-authors on academic publications using the developed infrastructure.

  • Have the opportunity to lead scientific projects if interested.
     

Ideal candidates will have:

  • A BA/BS/BTech in a technical or engineering field.

  • Experience in building embedded/real-time systems, signal processing, writing well-documented code, and working in collaborative teams is highly desirable.
     

Graduate Students
 

We are seeking up to 3 graduate students for all the projects outlined above. Email us if you are an admitted student or planning to apply to a PhD program at Rice.

Publications

​Preprints

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  1. N. P. Shah, M. S. Willsey, N. Hahn, F. Kamdar, D. T. Avansino, C. Fan, L. R. Hochberg, F. Willett, Jaimie M. Henderson A flexible intracortical brain-computer interface for typing using finger movements. bioRxiv April 2024. [paper]

  2. N. P. Shah, D. Avansino, F. Kamdar, C. Nicolas, A. Kapitonava, C. Vargas-Irwin, L. R. Hochberg, C. Pandarinath, K. Shenoy, F. R Willett, J. Henderson Pseudo-linear Summation explains Neural Geometry of Multi-finger Movements in Human Premotor Cortex. bioRxiv, October 2023 [Paper]

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Journal papers

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  1. M. S. Willsey, N. P. Shah, D. T. Avansino, N. V. Hahn, R. M. Jamiolkowski, F. B. Kamdar, L. R. Hochberg, F. R. Willett, J. M. Henderson. A real-time, high-performance brain-computer interface for finger decoding and quadcopter control. Nature Medicine January 2025. [pPaper]

  2. N. P. Shah*, A.J. Phillips*, S. Madugula, A. Lotlikar, A. R. Gogliettino, M. Hays, L. Grosberg, J. Brown, A. Dusi, P. Tandon, P. Hottowy, W. Dabrowski, A. Sher, A. M. Litke, S. Mitra, E.J. Chichilnisky. Precise control of neural activity using dynamically optimized electrical stimulation. eLife, November 2025 [Paper] (* indicates equal contribution)

  3. M. Zaidi, G. Aggarwal, N. P. Shah, O. Karniol-Tambour, G. Goetz, S. Madugula,  A. R. Gogliettino, E. G. Wu, A. Kling, N. Brackbill, A. Sher, A. M. Litke, E.J. Chichilnisky. Inferring retinal ganglion cell light response properties from intrinsic electrical features. Journal of Neural Engineering. August 2023. [Paper] 

  4. P. Yan, A. Akhoundi, N. P. Shah, P. Tandon, D. G. Muratore, E.J.Chichilnisky, Boris Murmann. Data Compression versus Signal Fidelity Tradeoff in Wired-OR Analog-to-Digital Compressive Arrays for Neural Recording. IEEE TBioCAS. July, 2023. [Paper] 

  5. S. Madugula,  R. Vilkhu, N. P. Shah, L. Grosberg, A. Kling, A. Gogliettino, H. Nguyen, P. Hottowy, A. Sher, A. Litke, E.J. Chichilnisky. Inference of Electrical Stimulation Sensitivity from Recorded Activity of Primate Retinal Ganglion Cells. Journal of Neuroscience. June 2023. [Paper] 

  6. S. Madugula, A. R. Gogliettino, M. Zaidi, G. Aggarwal, A. Kling, N. P. Shah, R. Vilkhu, M. Hays, H. Nguyan, V. Fan, E. G. Wu, P. Hottowy, A. Sher, A. M. Litke, R. A. Silva, E. J. Chichilnisky. Focal Electrical Stimulation of Human Retinal Ganglion Cells for Vision Restoration. Journal of Neural Engineering. December 2022. [Paper] 

  7. N. P Shah, N. Brackbill, R. Samarakoon, C. Rhoades, A. Kling, A. Sher, A. Litke, Y. Singer, J. Shlens, E.J. Chichilnisky. Individual Variability in the Neural Code of the Retina. Neuron, February 2022. [Paper] 

  8. N. P. Shah, E.J. Chichilnisky. Computational Challenges and Opportunities for a Bidirectional Artificial Retina. Journal of Neural Engineering, October 2020. [Paper] 

  9.  P. Tandon, N. Bhaskar, N. P. Shah, S. Madugula, L.E. Grosberg, V.H. Fan, P. Hottowy, A. Sher, A.M. Litke, E.J. Chichilnisky, S. Mitra. Automatic Identification and Avoidance of Axon Bundle Activation for Epiretinal Prosthesis. IEEE Trasactions on Neural Systems and Rehabilitation Engineering 2021. [Paper]

  10. N. Brackbill, C. Rhoades, A. Kling, N. P. Shah, A. Sher, A. M. Litke, E.J. Chichilnisky. Reconstruction of natural images from responses of primate retinal ganglion cells. eLife November 2020. [Paper]    

  11. N. P Shah, N. Brackbill, C. Rhoades, A. Kling, G. Goetz, A. Litke, A. Sher, E. P. Simoncelli, E.J. Chichilnisky. Inference of nonlinear receptive field subunits with spike-triggered clustering. eLife:9:e45743 March, 2020. [Paper][Code]

  12. C. Rhoades, N. P Shah, M. Manookin, N. Brackbill, A. Kling, G. Goetz, A. Sher, A. Litke, E.J. Chichilnisky. Unusual Physiological Properties of Smooth Monostratified Ganglion Cell Types in Primate Retina. Neuron, June 2019. [Paper]

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Conference papers

 

  1.  N. P. Shah, M. S. Willsey, N. Hahn, F. Kamdar, D. Avansino, Krishna Shenoy*, Jaimie Henderson*. A brain-computer typing interface using finger movements. IEEE NER, April 2023 [Paper]

  2. P. Vasireddy, A. Gogliettino, J. Brown, R. Vilkhu, S. Madugula, A.J. Phillips, S. Mitra, P. Hottowy, A. Sher, A. Litke, N. P. Shah, E.J. Chichilnisky. Efficient Modeling and Calibration of Multi-Electrode Stimuli for Epiretinal Implants. IEEE NER, April 2023 [Paper] (Oral presentation)

  3. A. Lotlikar, N. P. Shah, A. Gogliettino, R. Vilkhu, S. Madugula, L. Grosberg, P. Hottowy, A. Sher, A. Litke, E.J. Chichilnisky, Subhasish Mitra. Partitioned Temporal Dithering for Efficient Epiretinal Electrical Stimulation. IEEE NER, April 2023 [Paper]

  4. AJ Phillips, N. P Shah, M. Hays, S. Madugula, J. Brown, P. Hottowy, A. Sher, A. Litke, EJ Chichilnisky Spatially Multiplexed Electrical Stimulation to Reproduce the Neural Code in the Primate Retina. IEEE NER, April 2023 

  5. Pumiao Yan, N. P. Shah, Dante G. Muratore, Pulkit Tandon, E.J. Chichilnisky, Boris Murmann. Data Compression versus Signal Fidelity Tradeoff in Wired-OR ADC Arrays for Neural Recording. IEEE Biomedical Circuits and Systems Conference, October 2022 [Paper]

  6. N. P. Shah, S. Madugula, P. Hottowy, A. Sher, A. Litke, L. Paninski, E.J. Chichilnisky. Efficient Characterization of Electrically Evoked Responses for Neural Interfaces. NeurIPS, December 2019 [Paper] [Code]

  7. N. P. Shah, S. Madugula, L. Grosberg, G. Mena, P. Tandon, P. Hottowy, A. Sher, A. Litke, S. Mitra, E.J. Chichilnisky Optimization of Electrical Stimulation for a High-Fidelity Artificial Retina. IEEE NER, March 2019 [Paper]}(Invited for Plenary talk})

  8. N. P. Shah, S. Madugula, E.J. Chichilnisky, Y. Singer, J. Shlens. Learning a neural response metric for retinal prosthesis. ICLR, April 2018 [Paper]

  9. N. P. Shah, F. Alexandre. Reinforcement learning and dimensionality reduction: A model in computational neuroscience. IJCNN, July 2011[Paper]
        

Rice Brain Computer Interface Lab

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