Hanna-Leena Halme

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Education/Academic qualification

Tekn. toht., teknillinen fysiikka

Award Date: 30 Dec 2022

Tekn. lis., teknillinen fysiikka

Award Date: 25 Apr 2019

Dipl.ins., tietotekniikka

Award Date: 10 Feb 2014

Tekniikan kandidaatti, Bioinformaatioteknologian koulutusohjelma, Biologinen tekniikka, Aalto-yliopisto, Sähkötekniikan korkeakoulu

Award Date: 20 Feb 2012

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6 Article
1 Conference contribution
1 Doctoral Thesis
1 Licenciate's thesis

Methods for brain–computer interfaces utilizing MEG and motor imagery
Halme, H-L., 2022, Aalto University. 136 p.
Research output: Thesis › Doctoral Thesis › Collection of Articles

Open Access
The effect of visual and proprioceptive feedback on sensorimotor rhythms during BCI training
Halme, H. L. & Parkkonen, L., Feb 2022, In: PloS one. 17, 2, p. 1-20 20 p., e0264354.
Research output: Contribution to journal › Article › Scientific › peer-review

Open Access
File
4 Citations (Scopus)

23 Downloads (Pure)
Adaptive neural network classifier for decoding MEG signals
Zubarev, I., Zetter, R., Halme, H. L. & Parkkonen, L., 15 Aug 2019, In: NeuroImage. 197, p. 425-434 10 p.
Research output: Contribution to journal › Article › Scientific › peer-review

Open Access
File
28 Citations (Scopus)

315 Downloads (Pure)
Development of an MEG-based brain–computer interface
Halme, H-L., 2019, Aalto University. 47 p.
Research output: Thesis › Licenciate's thesis

Open Access
Across-subject offline decoding of motor imagery from MEG and EEG
Halme, H-L. & Parkkonen, L., 1 Dec 2018, In: Scientific Reports. 8, 1, p. 1-12 10087.
Research output: Contribution to journal › Article › Scientific › peer-review
28 Citations (Scopus)

39 Downloads (Pure)

Best doctoral theses and master's theses 2022 in the School of Science awarded
Aki Vehtari, Hanna-Leena Halme, Juha Kinnunen, Lauri Parkkonen, J.P. Pekola, Bayan Karimi, Aristides Gionis, Henri Schildt & Sabrina Maniscalco
21/03/2023
1 item of Media coverage
Press/Media: Media appearance
Developing machine-learning methods for the analysis of electromagnetic brain activity
Hanna-Leena Halme & Lauri Parkkonen
09/04/2021
1 item of Media coverage
Press/Media: Media appearance

Hanna-Leena Halme

Personal profile

Education/Academic qualification

Fingerprint

Research output

Methods for brain–computer interfaces utilizing MEG and motor imagery

The effect of visual and proprioceptive feedback on sensorimotor rhythms during BCI training

Adaptive neural network classifier for decoding MEG signals

Development of an MEG-based brain–computer interface

Across-subject offline decoding of motor imagery from MEG and EEG

Press/Media

Best doctoral theses and master's theses 2022 in the School of Science awarded

Developing machine-learning methods for the analysis of electromagnetic brain activity