TY - JOUR
T1 - Endogenous sensory discrimination and selection by a fast brain switch for a high transfer rate brain-computer interface
AU - Xu, Ren
AU - Jiang, Ning
AU - Dosen, Strahinja
AU - Lin, Chuang
AU - Mrachacz-Kersting, Natalie
AU - Dremstrup, Kim
AU - Farina, Dario
PY - 2016
Y1 - 2016
N2 - In this study, we present a novel multi-class brain-computer interface (BCI) for communication and control. In this system, the information processing is shared by the algorithm (computer) and the user (human). Specifically, an electro-tactile cycle was presented to the user, providing the choice (class) by delivering timely sensory input. The user discriminated these choices by his/her endogenous sensory ability and selected the desired choice with an intuitive motor task. This selection was detected by a fast brain switch based on real-time detection of movement-related cortical potentials from scalp EEG. We demonstrated the feasibility of such a system with a four-class BCI, yielding a true positive rate of ∼ 80% and ∼ 70%, and an information transfer rate of ∼ 7 bits/min and ∼ 5 bits/min, for the movement and imagination selection command, respectively. Furthermore, when the system was extended to eight classes, the throughput of the system was improved, demonstrating the capability of accommodating a large number of classes. Combining the endogenous sensory discrimination with the fast brain switch, the proposed system could be an effective, multi-class, gaze-independent BCI system for communication and control applications.
AB - In this study, we present a novel multi-class brain-computer interface (BCI) for communication and control. In this system, the information processing is shared by the algorithm (computer) and the user (human). Specifically, an electro-tactile cycle was presented to the user, providing the choice (class) by delivering timely sensory input. The user discriminated these choices by his/her endogenous sensory ability and selected the desired choice with an intuitive motor task. This selection was detected by a fast brain switch based on real-time detection of movement-related cortical potentials from scalp EEG. We demonstrated the feasibility of such a system with a four-class BCI, yielding a true positive rate of ∼ 80% and ∼ 70%, and an information transfer rate of ∼ 7 bits/min and ∼ 5 bits/min, for the movement and imagination selection command, respectively. Furthermore, when the system was extended to eight classes, the throughput of the system was improved, demonstrating the capability of accommodating a large number of classes. Combining the endogenous sensory discrimination with the fast brain switch, the proposed system could be an effective, multi-class, gaze-independent BCI system for communication and control applications.
KW - Journal Article
U2 - 10.1109/TNSRE.2016.2523565
DO - 10.1109/TNSRE.2016.2523565
M3 - Journal article
C2 - 26849869
SN - 1534-4320
VL - 24
SP - 901
EP - 910
JO - IEEE Transactions on Neural Systems and Rehabilitation Engineering
JF - IEEE Transactions on Neural Systems and Rehabilitation Engineering
IS - 8
ER -