activity
20232026
most citedAn Ultra-Low Power Wearable BMI System with Continual Learning Capabilities

5 citations · 5 across the 3 of their papers we have counts for

collaborators

5 papers

cs.NI2026

Memento: Communicating Sensor Observations Through Shared Memory

Chae Young Lee, Lan Mei, Yifan Yang +2

Wireless sensing systems often transmit highly redundant observations, making communication a bottleneck for low-power devices. We present Memento, a communication system that expl…

cs.AR2026

"Is Low Power Enough?" Rethinking Energy Efficiency in High-Fidelity Sensing

Chae Young Lee, Lan Mei, Jesse Fonseca +1

For two decades, long-lived sensor nodes have been built from the lowest-power parts, because their scalar readings take only milliseconds to acquire and send. Scientists now want…

eess.SP2024

Train-On-Request: An On-Device Continual Learning Workflow for Adaptive Real-World Brain Machine Interfaces

Lan Mei, Cristian Cioflan, Thorir Mar Ingolfsson +4

Brain-machine interfaces (BMIs) are expanding beyond clinical settings thanks to advances in hardware and algorithms. However, they still face challenges in user-friendliness and s…

eess.SP2024★ 5 cited

An Ultra-Low Power Wearable BMI System with Continual Learning Capabilities

Lan Mei, Thorir Mar Ingolfsson, Cristian Cioflan +4

Driven by the progress in efficient embedded processing, there is an accelerating trend toward running machine learning models directly on wearable Brain-Machine Interfaces (BMIs)…

eess.SP2023

Enhancing Performance, Calibration Time and Efficiency in Brain-Machine Interfaces through Transfer Learning and Wearable EEG Technology

Xiaying Wang, Lan Mei, Victor Kartsch +2

Brain-machine interfaces (BMIs) have emerged as a transformative force in assistive technologies, empowering individuals with motor impairments by enabling device control and facil…