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Found 9 papers, 2 papers with code
Enhancing Epileptic Seizure Detection with EEG Feature Embeddings
Here, we propose to enhance the seizure detection performance by learning informative embeddings of the EEG signal.
XTab: Cross-table Pretraining for Tabular Transformers
The success of self-supervised learning in computer vision and natural language processing has motivated pretraining methods on tabular data.
NeuralTree: A 256-Channel 0.227-$μ$J/Class Versatile Neural Activity Classification and Closed-Loop Neuromodulation SoC
Closed-loop neural interfaces with on-chip machine learning can detect and suppress disease symptoms in neurological disorders or restore lost functions in paralyzed patients.
Fast and accurate decoding of finger movements from ECoG through Riemannian features and modern machine learning techniques
In this work, we introduce the use of Riemannian-space features and temporal dynamics of electrocorticography (ECoG) signal combined with modern machine learning (ML) tools to improve the motor decoding accuracy at the level of individual fingers.
Tree in Tree: from Decision Trees to Decision Graphs
Decision trees have been widely used as classifiers in many machine learning applications thanks to their lightweight and interpretable decision process.
Closed-Loop Neural Prostheses with On-Chip Intelligence: A Review and A Low-Latency Machine Learning Model for Brain State Detection
The application of closed-loop approaches in systems neuroscience and therapeutic stimulation holds great promise for revolutionizing our understanding of the brain and for developing novel neuromodulation therapies to restore lost functions.
Unsupervised Domain Adaptation for Cross-Subject Few-Shot Neurological Symptom Detection
Modern machine learning tools have shown promise in detecting symptoms of neurological disorders.
Closed-Loop Neural Interfaces with Embedded Machine Learning
Neural interfaces capable of multi-site electrical recording, on-site signal classification, and closed-loop therapy are critical for the diagnosis and treatment of neurological disorders.
ResOT: Resource-Efficient Oblique Trees for Neural Signal Classification
We trained the resource-efficient oblique tree with power-efficient regularization (ResOT-PE) on three neural classification tasks to evaluate the performance, memory, and hardware requirements.
