Nashra Ahmad

ORCID: 0000-0001-7361-7130
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About
Contact & Profiles
Research Areas
  • Neuroscience and Music Perception
  • Diverse Music Education Insights
  • Neural dynamics and brain function
  • EEG and Brain-Computer Interfaces
  • Blind Source Separation Techniques
  • Embodied and Extended Cognition
  • Action Observation and Synchronization
  • Music Technology and Sound Studies
  • Music and Audio Processing
  • Musicology and Musical Analysis
  • Cultural Differences and Values
  • Language, Metaphor, and Cognition

Durham University
2025

Indian Institute of Technology Gandhinagar
2021-2022

Resonance, a powerful and pervasive phenomenon, appears to play major role in human interactions. This article investigates the relationship between physical mechanism of resonance experience resonance, considers possibilities for enhancing within human-robot We first introduce as widespread cultural scientific metaphor. Then, we review nature "sympathetic resonance" mechanism. Following this introduction, remainder is organized two parts. In part one, (including synchronization rhythmic...

10.3389/fnbot.2022.850489 article EN cc-by Frontiers in Neurorobotics 2022-04-27

Research on music psychology has increased exponentially over the past half century, providing insights a wide range of topics underpinning perception, cognition, and production music. This wealth research means we are now in place to develop specific, testable theories music, with potential impact our wider understanding human biology, culture, communication. However, development more widely applicable inclusive responses requires these be informed by data that is representative global...

10.1177/20592043251317180 article EN cc-by-nc Music & Science 2025-01-01

The study explores the effect of length rhythmic cycles, and relationship between familiarity, musicianship cycle structure in their perception. While impact has been explored for musical tones words, this extends to perception using four patterns (with 7, 8, 10 16 beats), all derived from North Indian Classical Music. results highlight that cultural exposure training (not culture) affect one’s ability learn differentiate patterns. Shorter cycles were learned recalled by Culturally Familiar...

10.31234/osf.io/ktz78_v1 preprint EN 2025-02-20

The study explores the effect of length rhythmic cycles, and relationship between familiarity, musicianship cycle structure in their perception. While impact has been explored for musical tones words, this extends to perception using four patterns (with 7, 8, 10 16 beats), all derived from North Indian Classical Music. results highlight that cultural exposure training (not culture) affect one’s ability learn differentiate patterns. Shorter cycles were learned recalled by Culturally Familiar...

10.31234/osf.io/ktz78_v2 preprint EN 2025-03-14

The article provides an open-source Music Listening- Genre (MUSIN-G) EEG dataset which contains 20 participants' continuous Electroencephalography responses to 12 songs of different genres (from Indian folk music Goth Rock western electronic), along with their familiarity and enjoyment ratings. participants include 16 males 4 females, average age 25.3 (+/-3.38). data was collected at the Institute Technology Gandhinagar, India, using 128 channels Hydrocel Geodesic Sensor Net (HCGSN)...

10.1016/j.dib.2022.108663 article EN cc-by-nc-nd Data in Brief 2022-10-08

Rhythmic cycles from North Indian Classical Music (NICM) commonly contain 6 to 16 beats per cycle. Despite their prominence in NICM, these long rhythmic remain unexplored rhythm perception research, which has concentrated mainly on Western meters or the short/fast characteristic of musical traditions like those Turkey and Balkans. Long NICM also raise questions about whether isochrony is considered easier perceive as compared non-isochrony (based research meters) remains so when are longer...

10.31234/osf.io/ay3tk preprint EN 2024-07-01

Modern neuroscience has shown that the brain is profoundly rhythmic and frequencies of neural rhythms are responsive to musical rhythms. We collected Electroencephalography (EEG) response on 12 naturalistic music stimuli (songs), from 20 participants. retrieved tempo its sub-harmonics our further used this information predict beats in using Machine Learning techniques. observed a hierarchy each songs, with specific beat frequency be dominant (i.e. higher magnitude) than others. This led us...

10.1109/bibm52615.2021.9669750 article EN 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 2021-12-09
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