Recent engineering and neuroscience applications have led to the development of brain–computer interface (BCI) systems that improve the quality of life of people with motor disabilities. In the same area, a significant number of studies have been conducted in identifying or classifying upper-limb movement intentions. On the contrary, few works have been concerned with movement intention identification for lower limbs. Notwithstanding, lower-limb neurorehabilitation is a major topic in medical settings, as some people suffer from mobility problems in their lower limbs, such as those diagnosed with neurodegenerative disorders, such as multiple sclerosis, and people with hemiplegia or quadriplegia. Particularly, the conventional pattern recognition (PR) systems are one of the most suitable computational tools for electroencephalography (EEG) signal analysis as the explicit knowledge of the features involved in the PR process itself is crucial for both improving signal classification performance and providing more interpretability. In this regard, there is a real need for outline and comparative studies gathering benchmark and state-of-art PR techniques that allow for a deeper understanding thereof and a proper selection of a specific technique. This study conducted a topical overview of specialized papers covering lower-limb motor task identification through PR-based BCI/EEG signal analysis systems. To do so, we first established search terms and inclusion and exclusion criteria to find the most relevant papers on the subject. As a result, we identified the 22 most relevant papers. Next, we reviewed their experimental methodologies for recording EEG signals during the execution of lower limb tasks. In addition, we review the algorithms used in the preprocessing, feature extraction, and classification stages. Finally, we compared all the algorithms and determined which of them are the most suitable in terms of accuracy.

/pdf/identification-of-lower-limb-motor-tasks-via-brain-computer-23wk4oyl.pdf

Identification of Lower-Limb Motor Tasks via Brain–Computer Interfaces: A Topical Overview

BCI System using a Novel Processing Technique Based on Electrodes Selection for Hand Prosthesis Control

In the rapidly advancing field of biomedical engineering, effective real-time control of artificial limbs is a pressing research concern. Addressing this, the current study introduces a pioneering method for augmenting task recognition in prosthetic control systems, combining a ReliefF-based Deep Neural Networks (DNNs) approach. This paper has leveraged the MILimbEEG dataset, a comprehensive rich source collection of EEG signals, to calculate statistical features of Arithmetic Mean (AM), Standard Deviation (SD), and Skewness (S) across various motor activities. Supreme Feature Selection (SFS), of the adopted time-domain features, was performed using the ReliefF algorithm. The highest scored DNN-ReliefF developed model demonstrated remarkable performance, achieving accuracy, precision, and recall rates of 97.4 %, 97.3 %, and 97.4 %, respectively. In contrast, a traditional DNN model yielded accuracy, precision, and recall rates of 50.8 %, 51.1 %, and 50.8 %, highlighting the significant improvements made possible by incorporating SFS. This stark contrast underscores the transformative potential of incorporating ReliefF, situating the DNN-ReliefF model as a robust platform for forthcoming advancements in real-time prosthetic control systems. 

Advancing task recognition towards artificial limbs control with ReliefF-based deep neural network extreme learning

Field Programmable Gate Array (FPGA) is an application that is based on chips that can process audio, video, and image. For FPGA systems, onboard memory may be used; however, it cannot be readily expanded by putting in extra cards. Since microcontrollers can read and write Secure Digital (SD) cards, they are more useful. The primary goal is to develop a low-cost, non-volatile, flash-memory, detachable, portable, and user-friendly storage solution for Field Programmable Gate Array (FPGA) that can store vast quantities of data. Verilog enables complete access to the SD card's contents, eliminating the need for a microcontroller or general-purpose CPU on-chip. In this work, Spartan 6 and Artix-7 FPGA are utilized. For this, the proposed study utilizes a Strontium 4GB micro SDHC (class 6) card. The SD card stores files in the FAT32 format. The objective is to extract a BMP image file from an SD card. The proposed design has achieved the hardware resources in 916 look-up tables (LUT) and 464 flip-flops.

SD Card Interface Using FPGA for Multimedia Applications

The improvement of hardware for the acquisition and processing of electroencephalography (EEG) has made its portability become a reality. This allows for studies to be carried outside lab settings, as well as many commercial applications. As recordings are done over extended periods, these devices generate large volumes of data, mainly if the neuronal activity is recorded through multiple channels. Machine learning (ML) techniques allow to effectively analyse and use this data for a wide range of applications. However the portability of these techniques can be challenging. In this article, we set out to review over 40 relevant articles where ML techniques in a diverse set of EEG applications that have successfully been incorporated into portable systems.

On-Chip Machine Learning for Portable Systems: Application to Electroencephalography-based Brain-Computer Interfaces

In the field of prosthetics, different technologies have been incorporated in recent years to improve their development and control, likewise the application of Field-Programmable Gate Arrays (FPGA) related to the Biomedicine field has increased due to its flexibility to perform multiple instructions in a reduced amount of time. This paper presents the implementation of a classification system based on FPGA capable of classifying characterized data, representing an imaginary motor task and a motor task in lower extremities. A three-layer feed-forward neural network was designed in Matlab, testing different architectures to assess the performance of the classifier, using methods such as the confusion matrix and the ROC curve.

Implementation of a Classification System of EEG Signals Based on FPGA

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BCI System using a Novel Processing Technique Based on Electrodes Selection for Hand Prosthesis Control

Implementation of a Classification System of EEG Signals Based on FPGA