Research teams from around the world reflect on their brain sensing setups.
By Evan M. Peck and Erin T. Solovey
setups of the future.
Brain sensing hard- ware is as varied as it is important to the research. Here we ask
representatives of five research
groups about their current
hardware and brain sensing
• Mick Grierson, Embodied Audio-
visual Interaction (EAVI) Group,
•Kiel Gilleade and Stephen Fairclough, School of Natural Science
and Psychology, Liverpool John
Moores University (LJMU).
•Sebastián Mealla and Sergi Jordà,
Music Technology Group, Universitat
Pompeu Fabra; Aleksander Väljamäe,
Laboratory of Brain-Computer Interfaces, Graz University of Technology.
• Lennart Nacke, Interaction Lab,
University of Saskatchewan.
• Erin Solovey, Human-Computer
Interaction Lab, Tufts University.
•Chi Thanh Vi, Interaction and
Graphics, University of Bristol.
WHAT DEVICE ARE
Mick Grierson: The
Embodied Audiovisual Interaction
group has been
doing a range of
research across new interaction technologies, including professional/medi-cal, commercial and DIY, in many cases
attempting to get comparisons between
professional and commercial hardware.
The EAVI lab has a g.Tec mobilab+, a
nine-channel research-grade electroencephalograph (EEG) that allows for
real-time acquisition of EEG data over
Bluetooth connections. This device has
been used for a range of brain-computer
interface (BCI) research, most specifically in the development of BCIs for music
using event-related potentials (ERPs). In
the last two years, the EAVI lab has been
experimenting further to apply P300
ERP detection on the Neurosky Mindset,
a low-cost commercial EEG device.
Kiel Gilleade and
Currently we use
which offers a four-channel setup for
EEG, heart rate, and
phy (EOG) sensing
through dry active
electrodes. We are
also interested in
systems using high-
er number of dry
electrodes, also in non-frontal areas,
and looking for ward to new technologi-
cal development in the BCI field.
(left), Sergi Jordà
Väljamäe: Currently we use Starlab’s
Enobio for sensing
EEG and heart-rate
signals. We are also interested in systems using higher number of dry electrodes, also in non-frontal areas.
Chi Thanh Vi: The
that we are using is
an Emotiv neuro-headset. This device
has 16 electrodes (14
channels and two
reference electrodes). Its effective bandwidth is from 0.16Hz to 43Hz; its internal hardware sampling frequency is
1028Hz and the SDK’s output frequency
is 128Hz. The device can measure emo-