eLife

Distinct cortical encoding of acoustic and electrical cochlear stimulation

Auditory cortex iEEG responses to pure tone stimuli in normal-hearing and deafened cochlear implant rats. A, Schematic of 60-channel cortical surface electrode arrays, covering 3.25 x 3.25 mm on the ...
eLife

The Spatial Frequency Representation Predicts Category Coding in the Inferior Temporal Cortex

Not revised: This Reviewed Preprint includes the authors’ original preprint (without revision), an eLife assessment, and public reviews. This study reports that IT neurons have biased representations ...
IEEE

Investigation on PCA and LDA Feature Extraction Algorithms with KNN Classifier Targetting to Improve Diagnostic Accuracy into Detection of Breast Cancer Malignancy

Abstract: This work aims to compare two different Feature Extraction Algorithms (FEAs) viz. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), using a K-Nearest Neighbor (KNN) ...
GitHub

Subspace Classifiers: PCA & LDA for Pose Estimation

These are applied to real-world classification problems, including pose (orientation) estimation from image data. Understand and implement dimensionality reduction techniques. Apply PCA and LDA to ...
GitHub

Computer Vision HW10: Face and Car Detection

For more detailed explanations, experimental procedures, and complete code implementations, please refer to the full project documentation in the PDF. Overfitting: Discusses how models can learn both ...
The New York Times

Paris Paralympics 2024: How the classification system works

PR1, W1, T51, F58, SL4, KL3, SM11. This is not a test to crack a code. But you will see a series of letter and number combinations while engaging with the Paralympics in Paris. At the Olympics, there ...
IEEE

A High Accuracy and Real-Time sEMG-based Hand Gesture Classifier using LDA-based Template Matching with Adaptive Majority Vote and Online Data Augmentation

Abstract: Surface electromyography (sEMG)-based gesture classification is promising in many emerging applications such as prosthetic control and Augmented Reality (AR) / Virtual Reality (VR). While ...
Frontiers

Exploring EEG-based motor imagery decoding: a dual approach using spatial features and spectro-spatial Deep Learning model IFNet

Introduction: In recent years, the decoding of motor imagery (MI) from electroencephalography (EEG) signals has become a focus of research for brain-machine interfaces (BMIs) and neurorehabilitation.
Frontiers

Kernel Density Estimation of Electromyographic Signals and Ensemble Learning for Highly Accurate Classification of a Large Set of Hand/Wrist Motions

The performance of myoelectric control highly depends on the features extracted from surface electromyographic (sEMG) signals. We propose three new sEMG features based on the kernel density estimation ...