BCIBench: A BCI Benchmarking Suite


Introduction

Increased demands for applications of brain computer interface (BCI) have led to growing attention towards their low-power embedded processing architecture design. Most clinical, wellness, and entertainment applications of BCI require wearable and portable devices. Better understanding of application characteristics in terms of computational complexity, memory usage, and power consumption can lead to more effective system designs for future wearable BCIs. For this purpose, we introduce BCIBench, a benchmarking suite which includes a wide range of algorithms used for pre-processing, feature extraction and classification in BCI applications, and in the related publications we analyzed the architectural characteristics of these algorithms such as performance, data-intensiveness and memory behavior. We provide insights into architectural components that can enhance the performance and reduce the power consumption of BCI embedded systems using these applications.

Contribution:

Embedded Signal Processing Laboratory , Electrical Engineering Department, University of Texas at Dallas

Overview


Download all source code, data & scripts for BCI Benchmark suite: BCIBench.rar

Preprocessing

Download all source code, data & scripts for Preprocessing: Pre_Processing.rar


Feature Extraction

Download all source code, data & scripts for Feature Extration: Feature_Extraction.rar


Classification

Related paper about Classification methods for BCI: Classification.pdf

Download all source code, data & scripts for Classification: Classification.rar


Application

Download all source code, data & scripts for End-to-end Applications: Ene_to_end_applications.rar


Related Publications


Acknowledgment