ELIMINATION OF OCULAR ARTIFACTS FROM EEG SIGNALS USING ADAPTIVE LEARNING TECHNIQUESPERFORMANCE OF QUANTUM DOT BASED INTERMEDIATE BAND SOLAR CELLS

Vineet Dahiya; Krishana Karak; Sahana Lokesh R

ELIMINATION OF OCULAR ARTIFACTS FROM EEG SIGNALS USING ADAPTIVE LEARNING TECHNIQUESPERFORMANCE OF QUANTUM DOT BASED INTERMEDIATE BAND SOLAR CELLS

Authors

Vineet Dahiya K.R. Mangalam University, Gurugram, Haryana, INDIA
Krishana Karak Sandip University (Sijoul)
Sahana Lokesh R Sri Siddhartha Academy of Higher Education

Keywords:

Engineering, Eeg Signals Using, Adaptive Learning, Electroencephalography (EEG)

Abstract

Mind-machine connections have captivated humans since their conception. From conception, this has been true. Neurobiology and engineering advances are making this notion more feasible. This allows future research to increase human mental and physical talents and even regain them. BCI research has several possible uses. These uses include entertainment, education, telepresence, human evolution, and debunking lies. Brain-computer interfaces were initially developed to help severely handicapped persons communicate. Early BCI systems had sluggish velocities, a high error rate, hypersensitivity to disturbances, and complexity, making it challenging to create real-world systems. New computer and bio-detecting technology has extended brain-computer interface applications (BCIs). Because of this, these technologies may be used for both assistive and public applications. The brain-computer interface (BCI) receives brain impulses, interprets them, and sends instructions to a computer depending on the brain signal. Sequence happens. Depending on the extent of intrusion needed, BCI techniques can be intrusive, semi-invasive, or non-invasive. Invasive brain-computer interfaces (BCIs) are inserted during neurosurgery to capture brain activity. BCIs investigate neurological patients. As the BCI is non-invasive, researchers are exploring its usage beyond restorative applications.

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ELIMINATION OF OCULAR ARTIFACTS FROM EEG SIGNALS USING ADAPTIVE LEARNING TECHNIQUESPERFORMANCE OF QUANTUM DOT BASED INTERMEDIATE BAND SOLAR CELLS