An insight into the hardware and software aspects of a BCI system with focus on ultra-low power bulk driven OTA and Gm-C based filter design, and a detailed review of the recent AI/ML techniques

Singh, Nameirakpam Premjit; Gautam, Akash; Sharan, Tripurari

doi:10.1016/b978-0-323-91197-9.00015-1

Cited by 3 publications

References 76 publications

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Brain-computer interface prototype to support upper limb rehabilitation processes in the human body

Camargo-Vargas,

Callejas-Cuervo,

Alarcón-Aldana

2023

Int. j. inf. tecnol.

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The high potential for creating brain-computer interfaces (BCIs) and video games for upper limb rehabilitation has been demonstrated in recent years. In this work, we describe the implementation of a prototype BCI with feedback based on a virtual environment to control the lateral movement of a character by predicting the subject’s motor intention. The electroencephalographic signals were processed employing a Finite Impulse Response (FIR) filter, Common Spatial Patterns (CSP), and Linear Discriminant Analysis (LDA). Also, a video game was used as a virtual environment, which was written in C# on the Unity3D platform. The test results showed that the prototype implemented based on electroencephalographic signal acquisition has the potential to take on real-time applications such as avatar control or assistive devices, obtaining a maximum control time of 65 s. In addition, it was noticed that the feedback in an interface plays a crucial role, since it helps the person not only to feel motivated, but also to learn how to have a more consistent motor intention and when little calibration data is recorded, the probability that the system makes erroneous predictions increases. These results demonstrate the usefulness of the development as support for people who require some treatment in the form of upper limb motor rehabilitation, and that the use of virtual environments, such as video games, can motivate such people during the rehabilitation processes.

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Brain-computer interface prototype to support upper limb rehabilitation processes in the human body

Camargo-Vargas,

Callejas-Cuervo,

Alarcón-Aldana

2023

Int. j. inf. tecnol.

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Multidimensional Perspective to Data Preprocessing for Model Cognition Verity

Ajakwe,

Deji-Oloruntoba,

Olatunbosun

et al. 2024

Advances in Systems Analysis, Software Engineering, and High Performance Computing

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Reliable data analysis depends on effective data preparation, especially since AI-driven business intelligence depends on unbiased and error-free data for decision-making. However, developing a reliable dataset is a difficult task that requires expertise. Due to the costly damage a negligible error in data can cause to a system, a good understanding of the processes of quality data transformation is necessary. Data varies in properties, which determines how it is generated, the errors in it, and the transformations it needs to undergo before it is fed into a model. Also, most data used for analytics is sourced from public stores without means to verify its quality or what further steps need to be taken in preprocessing it for optimal performance. This chapter provides a detailed description of practical and scientific procedures to generate and develop quality data for different models and scenarios. Also, it highlights the tools and techniques to clean and prepare data for optimal performance and prevent unreliable data analytics outcomes.

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Developing a tablet-based brain-computer interface and robotic prototype for upper limb rehabilitation

Lakshminarayanan,

Ramu,

Shah

et al. 2024

PeerJ Computer Science

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Background The current study explores the integration of a motor imagery (MI)-based BCI system with robotic rehabilitation designed for upper limb function recovery in stroke patients. Methods We developed a tablet deployable BCI control of the virtual iTbot for ease of use. Twelve right-handed healthy adults participated in this study, which involved a novel BCI training approach incorporating tactile vibration stimulation during MI tasks. The experiment utilized EEG signals captured via a gel-free cap, processed through various stages including signal verification, training, and testing. The training involved MI tasks with concurrent vibrotactile stimulation, utilizing common spatial pattern (CSP) training and linear discriminant analysis (LDA) for signal classification. The testing stage introduced a real-time feedback system and a virtual game environment where participants controlled a virtual iTbot robot. Results Results showed varying accuracies in motor intention detection across participants, with an average true positive rate of 63.33% in classifying MI signals. Discussion The study highlights the potential of MI-based BCI in robotic rehabilitation, particularly in terms of engagement and personalization. The findings underscore the feasibility of BCI technology in rehabilitation and its potential use for stroke survivors with upper limb dysfunctions.

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An insight into the hardware and software aspects of a BCI system with focus on ultra-low power bulk driven OTA and Gm-C based filter design, and a detailed review of the recent AI/ML techniques

Cited by 3 publications

References 76 publications

Brain-computer interface prototype to support upper limb rehabilitation processes in the human body

Brain-computer interface prototype to support upper limb rehabilitation processes in the human body

Multidimensional Perspective to Data Preprocessing for Model Cognition Verity

Developing a tablet-based brain-computer interface and robotic prototype for upper limb rehabilitation

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