SSVEP Signal Detection for BCI Application

Prasad, PG; Swarnkar, Radhika; Prasad, K. Rajendra; Radhakrishnan, Menaka; Hashmi, Mohammad Farukh; Keskar, Avinash G.

doi:10.1109/iacc.2017.0126

Cited by 8 publications

(2 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In this domain, brain signals primarily derived from motor-imagery tasks are used to enable the control of a prosthesis (Hong and Khan 2017) such as a robotic arm for users with spinal cord injury (Nicolas-Alonso and Gomez-Gil 2012;Müller-Putz et al 2005 or as input to controllers for wheelchairs (Carlson and Millan 2013). In these applications, machine learning classifiers such as LDA and K-nearest neighbors (Bhattacharyya et al 2010), Support Vector Machine (SVM), and Convolutional Neural Networks (CNN) (Tang et al 2017) are used in conjunction with specific features of brain activity such as the P300 response (Thulasidas et al 2006) or steady-state evoked potentials (SSVEP) (Prasad et al 2017) to classify brain signals and provide the input that drives the prosthesis. Currently, training these classifiers involves using synthetic laboratory-based tasks to elicit the desired response, resulting in lengthy training sessions, large preprocessed data sets and increased computational cost.…”

Section: Brain-computer Interfacesmentioning

confidence: 99%

Enhancing Sustained Attention

et al. 2021

View full text Add to dashboard Cite

Arguably, automation is fast transforming many enterprise business processes, transforming operational jobs into monitoring tasks. Consequently, the ability to sustain attention during extended periods of monitoring is becoming a critical skill. This manuscript presents a Brain-Computer Interface (BCI) prototype which seeks to combat decrements in sustained attention during monitoring tasks within an enterprise system. A brain-computer interface is a system which uses physiological signals output by the user as an input. The goal is to better understand human responses while performing tasks involving decision and monitoring cycles, finding ways to improve performance and decrease on-task error. Decision readiness and the ability to synthesize complex and abundant information in a brief period during critical events has never been more important. Closed-loop control and motivational control theory were synthesized to provide the basis from which a framework for a prototype was developed to demonstrate the feasibility and value of a BCI in critical enterprise activities. In this pilot study, the BCI was implemented and evaluated through laboratory experimentation using an ecologically valid task. The results show that the technological artifact allowed users to regulate sustained attention positively while performing the task. Levels of sustained attention were shown to be higher in the conditions assisted by the BCI. Furthermore, this increased cognitive response seems to be related to increased on-task action and a small reduction in on-task errors. The research concludes with a discussion of the future research directions and their application in the enterprise.

show abstract

Section: Brain-computer Interfacesmentioning

confidence: 99%

Enhancing Sustained Attention

et al. 2021

View full text Add to dashboard Cite

show abstract

“…En la literatura, existen varios tipos de clasificadores para estudiar el fenómeno de SSVEP (da Cruz, Wan, Wong, & Cao, 2015) (Shyam Prasad, et al, 2017). Para seleccionar el clasificador con mejor rendimiento, se realizó un análisis estadístico de los clasificadores SSVEP que se reportan en la literatura y con base en las conclusiones de éste, se optó por seleccionar los clasificadores simple tree (ST) y support vector machine (SVM) debido que ambos tienen una velocidad de predicción rápida, un uso de memoria bajo para el ST y medio para el SVM y una fácil interpretación.…”

Section: Clasificaciónunclassified

Análisis del desempeño de sistema de detección de señal SSVEP utilizando clasificadores árbol simple y máquina de vectores de soporte

Sáenz

González

Murguía

2019

recibe

View full text Add to dashboard Cite

Las interfaces cerebro-computadora (BCI) son sistemas que interpretan patrones de actividad cerebral para facilitar a personas con discapacidad motora o del habla, la comunicación o el control de dispositivos. Una forma de comunicación exitosa han sido los deletreadores basados en potenciales evocados de estado estable (SSVEP por sus siglas en inglés) que parten de la generación de estímulos visuales para provocar determinada actividad cerebral, registrarla e interpretarla mediante algoritmos de clasificación. Dentro de estos sistemas falta realizar un análisis para determinar un clasificador que tenga una relación tiempo de procesamiento y desempeño eficientes para aplicaciones en entornos controlados. En este trabajo se parte de la generación y registro de señales SSVEP, para realizar un análisis de desempeño de los clasificadores de árbol simple ST, y de máquinas de vectores de soporte SVM, los cuales son algoritmos de baja complejidad computacional encontrados en los métodos de análisis de SSVEP. De acuerdo a los resultados obtenidos, el porcentaje promedio de acierto del ST para una señal de 10 s fue de 84.58% para el canal O1 y un 78.75% para el O2; mientras que el del SVM fue 78.33%.

show abstract