Cognitive Control Signals for Neural Prosthetics

By Sam Musallam, B. Corneil, Bradley Greger, H. Scherberger, Richard A. Andersen. Science, 2004

647 citations recorded by the source corpus.

Recent development of neural prosthetics for assisting paralyzed patients has focused on decoding intended hand trajectories from motor cortical neurons and using this signal to control external devices. In this study, higher level signals related to the goals of movements were decoded from three monkeys and used to position cursors on a computer screen without the animals emitting any behavior. Their performance in this task improved over a period of weeks. Expected value signals related to fluid preference, the expected magnitude, or probability of reward were decoded simultaneously with the intended goal. For neural prosthetic applications, the goal signals can be used to operate computers, robots, and vehicles, whereas the expected value signals can be used to continuously monitor a paralyzed patient's preferences and motivation.

Subfields: Biomedical Engineering, Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/6116c024c88be033cd590ee739ff87e462ab06aa

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In Biomedical Engineering

In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

Citations in the corpus

Noninvasive neuroimaging enhances continuous neural tracking for robotic device control (2019, background): “BCI learning rates can vary among individuals, but it is generally thought that a user’s motivation and cognitive arousal play significant roles in the process of skill acquisition and eventual task performance (15, 16).”
Human Reinforcement Learning Subdivides Structured Action Spaces by Learning Effector-Specific Values (2009, background): “…is specialized for other effectors such as reaching and grasping (Murata et al., 1996; Connolly et al., 2003; Kalaska et al., 2003; Pesaran et al., 2006; Cui and Andersen, 2007), and more work is needed to identify the relationship of these areas to expected value (but see Musallam et al., 2004).”
A survey of signal processing algorithms in brain–computer interfaces based on electrical brain signals (2007, methodology): “…Polak 1997) None (Anderson et al 1995a, Panuccio et al 2002) Filtering − rectification − thresholding (Karniel et al 2002, Kositsky et al 2003, Reger et al 2000a, 2000b) Averaging (Laubach et al 2000, Otto et al 2003, Vetter et al 2003) TFR methods (Laubach et al 2000, Musallam et al 2004) None (”
The Science of Neural Interface Systems (2009, positive): “Neural interface experiments using recordings from primary motor cortex (Taylor et al. 2002) and posterior parietal cortex (Musallam et al. 2004) have documented modest but significant linear increases in percent correct performance ranging from 0.08 to 0.9 percentage points per recording session…”
Brain–computer interfaces as new brain output pathways (2007, methodology): “…BCI studies have adopted the process-control strategy (e.g. Table 1), while non-invasive BCI studies using the P300 evoked potential (e.g. Farwell & Donchin, 1988) and a few invasive studies using cortical neuron activity (e.g. Musallam et al. 2004) have adopted the goal-selection strategy.”
Control of a humanoid robot by a noninvasive brain–computer interface in humans (2008, background): “…this article are in colour only in the electronic version) Advances in neuroscience and computer technology have made possible a number of recent demonstrations of direct brain control of devices such as a cursor on a computer screen [1] [2] [3] [4] [5] and various prosthetic devices [6] [7] [8].”
Single-Neuron Stability during Repeated Reaching in Macaque Premotor Cortex (2007, methodology): “The current study examined the caudal aspect of PMd and the rostral aspect of M1, and instability found was not correlated with position on the electrode array.”
Neuronal Reward and Decision Signals: From Theories to Data. (2015, background): “Reward affects neuronal activity differentiating between different movement parameters during the instruction, preparation and execution of action in prefrontal and premotor cortex (FIGURE 20, D AND E) (135, 282, 313, 348, 476, 606, 627), anterior and posterior cingulate cortex (354, 542), parietal ”
A high-performance brain–computer interface (2006, background): “This type of control is appropriate for communication prostheses and beneﬁts from not having to estimate unnecessary parameters such as continuous trajectory 4,11 .”
Lower Limb Wearable Robots for Assistance and Rehabilitation: A State of the Art (2016, methodology): “project, EEG signals are used to discrete commands, such as the intended action [71], [72], while EMG signals are also used for controlling the lower limb exoskeleton.”
Cortical Ensemble Adaptation to Represent Velocity of an Artificial Actuator Controlled by a Brain-Machine Interface (2005, background): “evokes plastic changes in the activity patterns of cortical neurons (Taylor et al., 2002; Carmena et al., 2003; Musallam et al., 2004), a number of questions about cortical representation of artificial actuators remain to be addressed.”
The posterior parietal cortex: sensorimotor interface for the planning and online control of visually guided movements. (2006, background): “However, when sensory and motor vectors are issociated explicitly, both sensory and motor related activity are ound in LIP (Andersen et al., 1987; Gnadt & Andersen, 1988; hang & Barash, 2000), though other tasks have shown that the revalence of the latter increases as movement onset…”
Predicting Movement from Multiunit Activity (2007, methodology): “When only taskmodulated channels [determined using Kruskal–Wallis (KW) nonparametric one-way ANOVA] (Musallam et al., 2004; Sanchez et al., 2004) were analyzed, single-channel prediction accuracies were typically (and trivially) higher than those obtained for all channels that passed the above…”
High performance communication by people with paralysis using an intracortical brain-computer interface (2017, neutral): “…et al., 2011 ; Wang et al., 2013 ), and intracortical electrical signals ( Serruya et al., 2002 ; Taylor et al., 2002 ; Carmena et al., 2003 ; Musallam et al., 2004 ; Santhanam et al., 2006 ; Hochberg et al., 2006 ; Ganguly et al., 2011 ; O’Doherty et al., 2011 ; Gilja et al., 2012 ;…”