Table 1. | High performance communication by people with paralysis using an intracortical brain-computer interface

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High performance communication by people with paralysis using an intracortical brain-computer interface

Table 1.

Affiliation details

Stanford University, United States; Emory University and Georgia Institute of Technology, United States; Emory University, United States; Massachusetts General Hospital, United States; Brown University, United States; Rehabilitation R&D Service, Department of VA Medical Center, United States; Case Western Reserve University, United States; Louis Stokes VA Medical Center, United States; Harvard Medical School, United States
Table 1.

Survey of BCI studies that measure typing rates (correct characters per minute; ccpm), bitrates, or information transfer rates for people with motor impairment. Number ranges represent performance measurements across all participants for a given study. Communication rates could be further increased by external algorithms such as word prediction or completion. As there are many such algorithms, the current work excluded word prediction or completion to focus on measuring the performance of the underlying system. The most appropriate points of comparison, when available, are bitrates, which are independent of word prediction or completion algorithms. Similarly, information transfer rates are also a meaningful point of comparison, though they are less reflective of practical communication rates than bitrate (which takes into account the need to correct errors; detailed in Nuyujukian et al. (2015); Townsend et al. (2010)). For the current work, and for Jarosiewicz et al. 2015, we also break down performance by individual participant to facilitate direct comparisons (denoted by italics). As shown, performance in the current study outperforms all previous BCIs tested with people with motor impairment. *These numbers represent performance when measured using a denser grid (9 × 9; Figure 3—figure supplement 2 and Video 10). **For this study, reported typing rates included word prediction / completion algorithms. ***Number range represents the range of performance reported for the single study participant. ****Other reported numbers included word prediction / completion algorithms. †Acronyms used: ReFIT-KF: Recalibrated Feedback Intention-trained Kalman Filter. HMM: Hidden Markov Model. CLC: Closed-loop Calibration. LDA: Linear Discriminant Analysis. RTI: Retrospective Target Inference. DS: Dynamic Stopping.


StudyParticipant(s)Recording modalityControl modalityEtiology of
motor impairment
Average typing rate (ccpm)Average bitrate (bps)Average ITR (bps)
This studyaverage
(N = 3)
intracorticalReFIT-KF+HMMALS (2), SCI (1)
‘‘T7(No HMM)ALS13.51.41.4
Bacher et al., 2015S3intracorticalCLC+LDAbrainstem stroke9.4--
Jarosiewicz et al., 2015average (N = 4)intracorticalRTI+LDAALS (2),
brainstem stroke (2)
‘‘S3brainstem stroke‘‘0.58-
‘‘T2brainstem stroke‘‘0.19-
Nijboer et al., 2008N = 4EEGP300ALS1.5–4.1-0.08–0.32
Townsend et al., 2010N = 3EEGP300ALS-0.05–0.22-
Münßinger et al., 2010N = 3EEGP300ALS--0.02–0.12
Mugler, et al. 2010N = 3EEGP300ALS--0.07–0.08
Pires et al., 2011N = 4EEGP300ALS (2), cerebral palsy (2)--0.24–0.32
Pires et al., 2012N = 14EEGP300ALS (7), cerebral palsy (5),
Duchenne muscular
dystrophy (1), spinal cord injury (1)
Sellers et al., 2014N = 1EEGP300brainstem stroke0.31–0.93***--
McCane et al., 2015N = 14EEGP300ALS--0.19
Mainsah et al., 2015N = 10EEGP300-DSALS--0.01–0.60
Vansteensel et al., 2016N = 1subdural ECoGLinear ClassifierALS1.15****-0.21