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This article is from Biomedical Optics Express , volume 3 . Abstract A novel brush optode was designed and demonstrated to overcome poor optical contact with the scalp that can occur during functional near infrared spectroscopy (fNIRS) and imaging due to light obstruction by hair. The brush optodes were implemented as an attachment to existing commercial flat-faced (conventional) fiber bundle optodes. The goal was that the brush optodes would thread through hair and improve optical contact on subjects with dense hair. Simulations and experiments were performed to assess the magnitude of these improvements. FNIRS measurements on 17 subjects with varying hair colors (blonde, brown, and black) and hair densities (0–2.96 hairs/mm2) were performed during a finger tapping protocol for both flat and brush optodes. In addition to reaching a study success rate of almost 100% when using the brush optode extensions, the measurement setup times were reduced by a factor of three. Furthermore, the brush optodes enabled improvements in the activation signal-to-noise ratio (SNR) by up to a factor of ten as well as significant (p 

This study aims to design, develop, and test a portable, lightweight, noninvasive neuroimaging system that supports simultaneous electroencephalogram (EEG) and functional NIR spectroscopic (fNIRS) acquisition for biological or cognitive neuroscience studies in operational environments. The system features novel EEG/NIRS electrodes, known as electro-opodes, and miniaturized supporting hardware/software. In the past few years, our team, composed of faculty, postdoctoral fellows and graduate students, has designed and developed dry EEG and fNIR sensors that allow non-invasive and non-intrusive acquisition of EEG and fNIR signals. We have also designed the form factor of Electro-optodes that integrates the EEG and fNIR sensors in an elastic cap. We further evaluated the quality of EEG signals acquired by the dry spring-loaded EEG sensors and fNIR sensors through three experiments. This study has also made a lot of progress in designing and developing a data-acquisition VLSI chip that can acquire, amplify, digitize and process EEG and NIRS data. Lastly, to improve the fidelity of the EEG recordings, we have developed and implemented real-time artifact correction algorithms, Artifact Subspace Reconstruction, for online and real-time rejection of artifacts that often contaminate EEG signals recorded in real-world environments.