2024
- Jul 14 - Semantic encoding in single cell level
- Apr 5 - A Cross-Modal approach to silent speech with LLM-Enhanced recognition
2023
- Dec 17 - Neurips 2023 neuro-ml round up
- Feb 26 - PCA vs. FA: Theoretical and practical differences
- Feb 26 - Matrix Cookbook
- Feb 13 - Neuronal correlations and dimensionality reduction
2022
- Nov 1 - Signal Processing Notes 2
- Jun 6 - Physiology of endurance training
- Jan 28 - Challenge Point Framework for motor learning
- Jan 21 - The Eighty Five Percent Rule for optimal learning
2018
- Sep 30 - Linear Algebra Intuition
- Aug 22 - Latex biblography tricks
- May 5 - Signal Processing Notes
- Feb 22 - Hints from biology
- Jan 31 - More linear models
- Jan 30 - Generalized Linear Model (GLM) - ANCOVA and Multiple comparison
2017
- Dec 4 - Even-Chen et al 2017: Augmenting intracortical brain-machine interface with neurally driven error detectors
- Oct 14 - More Jekyll and CSS
- Oct 14 - Chi-squared post-hoc test, and how trolly statistical testing is
- Aug 9 - BMI skill acquisition through stimulation
- Jul 27 - Assisted MCMC -- Learning from human preferences
- Feb 24 - Using FemtoBeacon with ROS
2016
- Dec 17 - Lebedev, Carmena et al 2005: Cortical ensemble adaptation to represent velocity of an artificial actuator controlled by a brain-machine interface
- Sep 15 - Notes on comparison metrics
- May 7 - Platt & Glimcher 1999: Neural correlates of decision variables in parietal cortex
- May 7 - McNiel, [...] & Francis 2016: Classifier performance in primary somatosensory cortex towards implementation of a reinforcement learning based brain machine interface
- May 1 - Fogassi, Rizzolatti 2005: Parietal Lobe: From Action Organization to Intention Understanding
- Apr 30 - Chavarriaga, Sobolewski & Millan 2014: Errare machinale est: the use of error-related potentials in brain-machine interfaces
- Apr 28 - Dadarlat, O'Doherty & Sabes 2015: A learning-based approach to artificial sensory feedback leads to optimal integration
- Apr 21 - Linear Algebra Notes
- Apr 12 - Optimum mean square linear filter notes
- Apr 10 - Notes on Learning from Data
- Apr 5 - Wireless Headstage: Validation of signal quality IV - Results
- Apr 4 - Wireless Headstage: Validation of signal quality III - Computation and Methods
- Mar 31 - Wireless Headstage: Validation of signal quality II - Metrics
- Mar 27 - Wireless Headstage: Validation of signal quality I
- Feb 17 - Compilation of unconventional electrodes
- Feb 17 - The Brain Electric - Science journalism on the field of BMI
- Jan 28 - Koralek et al 2012: Corticostriatal plasticity is necessary for learning intentional neuroprosthetic skills
- Jan 27 - Golub et al 2016: Brain-computer interfaces for dissecting cognitive processes underlying sensorimotor control
- Jan 26 - Wireless Headstage: gtkclient notes
- Jan 26 - Wireless Headstage: Generate oscillations by setting IIR coefficients
- Jan 26 - Wireless Headstage: Spike sorting via PCA and spike detection with SAA
- Jan 26 - Wireless Headstage: Blackfin BF532 headstage firmware setup
- Jan 26 - Wireless Headstage: Radio transmission protocol between headstage and gtkclient.
- Jan 26 - Wireless Headstage: Least-Mean Squared (LMS) adaptive noise cancellation.
- Jan 6 - Solving jekyll highlight line numbers
- Jan 6 - Wireless Headstage: Implementing direct-form I biquad IIR filters
2015
- Dec 25 - Wireless Headstage: Blackfin BF532 communcation with Intan RHD2132 via SPI simulation via SPORT
- Dec 24 - Wireless Headstage: AGC with Blackfin BF532 and Intan RHD2132
- Jul 28 - Shenoy, Kaufman, Sahani, Churchland 2011: A dynamical systems view of motor preparation: Implications for neural prosthetic system design
- Jul 28 - Shenoy, Carmena 2014: Combining Decoder Design and Neural Adaptation in Brain-Machine Interfaces
- Jul 28 - Raspopovic, Micera 2014: Restoring Natural Sensory Feedback in Real-Time Bidirectional Hand Prostheses
- Jul 28 - Orsborn, Carmena 2014: Closed-loop decoder adaptation shapes neural plasticity for skillful neuroprosthetic control
- Jul 28 - Gowda, Carmena 2014: Designing Dynamical Properties of Brain–Machine Interfaces to Optimize Task-Specific Performance
- Jul 27 - Sanchez 2014 - Using Reinforcement Learning to Provide Stable Brain-Machine Interface Control Despite Neural Input Reorganization
- Jul 27 - Sanchez 2013 - Towards autonomous neuroprosthetic control using Hebbian reinforcement learing
- Jul 27 - Ganguly, Carmena 2011: Reversible large-scale modification of cortical networks during neuroprosthetic control
- Jul 27 - Francis 2015 - Toward an Autonomous Brain Machine Interface: Integrating Sensorimotor Reward Modulation and Reinforcement Learning
- Jul 26 - Sanchez 2011: Control of a Center-Out Reaching Task Using a Reinforcement Learning Brain-Machine Interface
- Jul 26 - DiGiovanna 2009: Coadaptive Brain-Machine Interface via Reinforcement Learning
- Jul 26 - Deep Learning - Review by LeCun, Bengio, and Hinton
- Jul 13 - SW Ch. 8: What provides limb stability?
- Jul 13 - SW Ch. 7: What generates force and feedback?
- Jul 13 - KS Ch. 37: Voluntary Movement - The Primary Motor Cortex
- Jul 13 - KS Ch. 33: The organization and planning of movement
- Jul 12 - Generating tags in Jekyll
- Jul 5 - My first post