Author

Kanjar De

Luleå University of Technology

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Neuroscience· 3

Signature Assays, Organisms & Methods

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Assay:electronic health records· 3 Research Focus:database / resource· 2 Approach:machine learning (classical)· 2 Research Focus:benchmarking· 1 Approach:experimental assay· 1 Organism:human· 1

Frequent Co-Authors

Foteini LiwickiiD· 3 shared Marcus LiwickiiD· 3 shared Rajkumar SainiiD· 3 shared Vibha GuptaiD· 3 shared Sumit Rakesh· 2 shared Ashish Ranjan MishraiD· 1 shared Debashis Das ChakladariD· 1 shared Hamam MokayediD· 1 shared

Activity by Year

2022-2024 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 39 mutual co-author links

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3 shared papersNeuroscience

Foteini LiwickiORCID

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3 papers

Recent·Most cited

SignEEG v1.0 : Multimodal Electroencephalography and Signature Database for Biometric Systems

Ashish Ranjan Mishra, Rakesh Kumar, Vibha Gupta, Sameer M. Prabhu, Richa Upadhyay, Prakash Chandra Chhipa, Sumit Rakesh, Hamam Mokayed, Debashis Das Chakladar, Kanjar De, Marcus Liwicki, Foteini Liwicki, Rajkumar Saini

The paper introduces SignEEG v1.0, a multimodal dataset combining EEG and handwritten signatures for biometric systems, showing improved robustness against forgery through machine learning classifiers.

Scientific Data · Jul 2, 2024Peer-reviewedNeuroscience

Bimodal electroencephalography-functional magnetic resonance imaging dataset for inner-speech recognition

Foteini Liwicki, Vibha Gupta, Rajkumar Saini, Kanjar De, Nosheen Abid, Sumit Rakesh, Scott Wellington, Holly Wilson, Marcus Liwicki, Johan Eriksson

This paper introduces a bimodal EEG-fMRI dataset for inner-speech recognition, collected from four participants during inner-speech tasks.

Scientific Data · Jun 13, 2023Peer-reviewedNeuroscience

Rethinking the Methods and Algorithms for Inner Speech Decoding - and Making them Reproducible

Foteini Liwicki, Vibha Gupta, Rajkumar Saini, Kanjar De, Marcus Liwicki

This paper evaluates methods for decoding inner speech from EEG data, achieving 35.20% accuracy for vowel classification and 29.21% for word classification, with reproducible code and data.

NeuroSci · Apr 19, 2022Peer-reviewedNeuroscience