Creating new functional circuits for action via brain-machine interfaces

Volitional modulation of neuronal activity in the external globus pallidus by engagement of the cortical-basal ganglia circuit

Volitional modulation of neural activity is not confined to the cortex but extends to various brain regions. Yet, it remains unclear whether neurons in the basal ganglia structure, the external globus pallidus (GPe), can be volitionally controlled. Here, we employed a volitional conditioning task to compare the volitional modulation of GPe and primary motor cortex (M1) neurons as well as the underlying circuits and control mechanisms. The results revealed that the volitional modulation of GPe neuronal activity engaged both M1 and substantia nigra pars reticulata (SNr) neurons, indicating the involvement of the cortex-GPe-SNr loop. In contrast, the volitional modulation of M1 neurons primarily occurred through the engagement of M1 local circuitry. Furthermore, lesioning M1 neurons did not affect the volitional learning or volitional control signal in GPe, whereas lesioning of GPe neurons impaired the learning process for the volitional modulation of M1 neuronal activity at the intermediate stage. Additionally, lesion of GPe neurons enhanced M1 neuronal activity when performing the volitional control task without reward delivery and a random reward test. Taken together, our findings demonstrated that GPe neurons could be volitionally controlled by engagement of the cortical-basal ganglia circuit and inhibit learning process for the volitional modulation of M1 neuronal activity by regulating M1 neuronal activity. Thus, GPe neurons can be effectively harnessed for independent volitional modulation for neurorehabilitation in patients with cortical damage. KEY POINTS: The cortical-basal ganglia circuit contributes to the volitional modulation of GPe neurons. Volitional modulation of M1 neuronal activity mainly engages M1 local circuitry. Bilateral GPe lesioning impedes volitional learning at the intermediate stages. Lesioning of GPe neurons inhibits volitional learning process by regulating M1 neuronal activity.

The role of the nucleus basalis of Meynert in neuromodulation therapy: a systematic review from the perspective of neural network oscillations

As a crucial component of the cerebral cholinergic system and the Papez circuit in the basal forebrain, dysfunction of the nucleus basalis of Meynert (NBM) is associated with various neurodegenerative disorders. However, no drugs, including existing cholinesterase inhibitors, have been shown to reverse this dysfunction. Due to advancements in neuromodulation technology, researchers are exploring the use of deep brain stimulation (DBS) therapy targeting the NBM (NBM-DBS) to treat mental and neurological disorders as well as the related mechanisms. Herein, we provided an update on the research progress on cognition-related neural network oscillations and complex anatomical and projective relationships between the NBM and other cognitive structures and circuits. Furthermore, we reviewed previous animal studies of NBM lesions, NBM-DBS models, and clinical case studies to summarize the important functions of the NBM in neuromodulation. In addition to elucidating the mechanism of the NBM neural network, future research should focus on to other types of neurons in the NBM, despite the fact that cholinergic neurons are still the key target for cell type-specific activation by DBS.

Neural subspaces of imagined movements in parietal cortex remain stable over several years in humans

A crucial goal in brain-machine interfacing is long-term stability of neural decoding performance, ideally without regular retraining. Here we demonstrate stable neural decoding over several years in two human participants, achieved by latent subspace alignment of multi-unit intracortical recordings in posterior parietal cortex. These results can be practically applied to significantly expand the longevity and generalizability of future movement decoding devices.

Neural representation and modulation of volitional motivation in response to escalating efforts

Task-dependent volitional control of the selected neural activity in the cortex is critical to neuroprosthetic learning to achieve reliable and robust control of the external device. The volitional control of neural activity is driven by a motivational factor (volitional motivation), which directly reinforces the target neurons via real-time biofeedback. However, in the absence of motor behaviour, how do we evaluate volitional motivation? Here, we defined the criterion (ΔF/F) of the calcium fluorescence signal in a volitionally controlled neural task, then escalated the efforts by progressively increasing the number of reaching the criterion or holding time after reaching the criterion. We devised calcium-based progressive threshold-crossing events (termed 'Calcium PTE') and calcium-based progressive threshold-crossing holding-time (termed 'Calcium PTH') for quantitative assessment of volitional motivation in response to progressively escalating efforts. Furthermore, we used this novel neural representation of volitional motivation to explore the neural circuit and neuromodulator bases for volitional motivation. As with behavioural motivation, chemogenetic activation and pharmacological blockade of the striatopallidal pathway decreased and increased, respectively, the breakpoints of the 'Calcium PTE' and 'Calcium PTH' in response to escalating efforts. Furthermore, volitional and behavioural motivation shared similar dopamine dynamics in the nucleus accumbens in response to trial-by-trial escalating efforts. In general, the development of a neural representation of volitional motivation may open a new avenue for smooth and effective control of brain-machine interface tasks. KEY POINTS: Volitional motivation is quantitatively evaluated by M1 neural activity in response to progressively escalating volitional efforts. The striatopallidal pathway and adenosine A_2A receptor modulate volitional motivation in response to escalating efforts. Dopamine dynamics encode prediction signal for reward in response to repeated escalating efforts during motor and volitional conditioning. Mice learn to modulate neural activity to compensate for repeated escalating efforts in volitional control.

Neural correlates of user learning during long-term BCI training for the Cybathlon competition

Background

Brain-computer interfaces (BCIs) are systems capable of translating human brain patterns, measured through electroencephalography (EEG), into commands for an external device. Despite the great advances in machine learning solutions to enhance the performance of BCI decoders, the translational impact of this technology remains elusive. The reliability of BCIs is often unsatisfactory for end-users, limiting their application outside a laboratory environment.

Methods

We present the analysis on the data acquired from an end-user during the preparation for two Cybathlon competitions, where our pilot won the gold medal twice in a row. These data are of particular interest given the mutual learning approach adopted during the longitudinal training phase (8 months), the long training break in between the two events (1 year) and the demanding evaluation scenario. A multifaceted perspective on long-term user learning is proposed: we enriched the information gathered through conventional metrics (e.g., accuracy, application performances) by investigating novel neural correlates of learning in different neural domains.

Results

First, we showed that by focusing the training on user learning, the pilot was capable of significantly improving his performance over time even with infrequent decoder re-calibrations. Second, we revealed that the analysis of the within-class modifications of the pilot’s neural patterns in the Riemannian domain is more effective in tracking the acquisition and the stabilization of BCI skills, especially after the 1-year break. These results further confirmed the key role of mutual learning in the acquisition of BCI skills, and particularly highlighted the importance of user learning as a key to enhance BCI reliability.

Conclusion

We firmly believe that our work may open new perspectives and fuel discussions in the BCI field to shift the focus of future research: not only to the machine learning of the decoder, but also in investigating novel training procedures to boost the user learning and the stability of the BCI skills in the long-term. To this end, the analyses and the metrics proposed could be used to monitor the user learning during training and provide a marker guiding the decoder re-calibration to maximize the mutual adaptation of the user to the BCI system.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12984-022-01047-x.

Implementation of Neuro-Memristive Synapse for Long-and Short-Term Bio-Synaptic Plasticity

In this paper, we propose a complex neuro-memristive synapse that exhibits the physiological acts of synaptic potentiation and depression of the human-brain. Specifically, the proposed neuromorphic synapse efficiently imitates the synaptic plasticity, especially long-term potentiation (LTP) and depression (LTD), and short-term facilitation (STF) and depression (STD), phenomena of a biological synapse. Similar to biological synapse, the short- or long-term potentiation (STF and LTP) or depression (STD or LTD) of the memristive synapse are distinguished on the basis of time or repetition of input cycles. The proposed synapse is also designed to exhibit the effect of reuptake and neurotransmitters diffusion processes of a bio-synapse. In addition, it exhibits the distinct bio-realistic attributes, i.e., strong stimulation, exponentially decaying conductance trace of synapse, and voltage dependent synaptic responses, of a neuron. The neuro-memristive synapse is designed in SPICE and its bio-realistic functionalities are demonstrated via various simulations.

Adenosine A2A receptor blockade improves neuroprosthetic learning by volitional control of population calcium signal in M1 cortical neurons

Volitional control is at the core of brain-machine interfaces (BMI) adaptation and neuroprosthetic-driven learning to restore motor function for disabled patients, but neuroplasticity changes and neuromodulation underlying volitional control of neuroprosthetic learning are largely unexplored. To better study volitional control at annotated neural population, we have developed an operant neuroprosthetic task with closed-loop feedback system by volitional conditioning of population calcium signal in the M1 cortex using fiber photometry recording. Importantly, volitional conditioning of the population calcium signal in M1 neurons did not improve within-session adaptation, but specifically enhanced across-session neuroprosthetic skill learning with reduced time-to-target and the time to complete 50 successful trials. With brain-behavior causality of the neuroprosthetic paradigm, we revealed that proficiency of neuroprosthetic learning by volitional conditioning of calcium signal was associated with the stable representational (plasticity) mapping in M1 neurons with the reduced calcium peak. Furthermore, pharmacological blockade of adenosine A_2A receptors facilitated volitional conditioning of neuroprosthetic learning and converted an ineffective volitional conditioning protocol to be the effective for neuroprosthetic learning. These findings may help to harness neuroplasticity for better volitional control of neuroprosthetic training and suggest a novel pharmacological strategy to improve neuroprosthetic learning in BMI adaptation by targeting striatal A_2A receptors.

Memristive Imitation of Synaptic Transmission and Plasticity

In this paper, a memristive artificial neural circuit imitating the excitatory chemical synaptic transmission of biological synapse is designed. The proposed memristor-based neural circuit exhibits synaptic plasticity, one of the important neurochemical foundations for learning and memory, which is demonstrated via the efficient imitation of short-term facilitation and long-term potentiation. Moreover, the memristive artificial circuit also mimics the distinct biological attributes of strong stimulation and deficient synthesis of neurotransmitters. The proposed artificial neural model is designed in SPICE, and the biological functionalities are demonstrated via various simulations. The simulation results obtained with the proposed artificial synapse are similar to the biological features of chemical synaptic transmission and synaptic plasticity.

The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users

This work aims at corroborating the importance and efficacy of mutual learning in motor imagery (MI) brain–computer interface (BCI) by leveraging the insights obtained through our participation in the BCI race of the Cybathlon event. We hypothesized that, contrary to the popular trend of focusing mostly on the machine learning aspects of MI BCI training, a comprehensive mutual learning methodology that reinstates the three learning pillars (at the machine, subject, and application level) as equally significant could lead to a BCI–user symbiotic system able to succeed in real-world scenarios such as the Cybathlon event. Two severely impaired participants with chronic spinal cord injury (SCI), were trained following our mutual learning approach to control their avatar in a virtual BCI race game. The competition outcomes substantiate the effectiveness of this type of training. Most importantly, the present study is one among very few to provide multifaceted evidence on the efficacy of subject learning during BCI training. Learning correlates could be derived at all levels of the interface—application, BCI output, and electroencephalography (EEG) neuroimaging—with two end-users, sufficiently longitudinal evaluation, and, importantly, under real-world and even adverse conditions.

Noninvasive brain–computer interface (BCI) based on imagined movements can restore functions lost to disability by enabling spontaneous, direct brain control of external devices without risks associated with surgical implantation of neural interfaces. We hypothesized that, contrary to the popular trend of focusing on the machine learning aspects of BCI training, a comprehensive mutual learning methodology could strongly promote users’ acquisition of BCI skills and lead to a system able to succeed in real-world scenarios such as the Cybathlon event, the first international competition for disabled pilots in control of assistive technology. Two severely impaired participants with chronic spinal cord injury (SCI) were trained following our mutual learning approach to control their avatar in a virtual BCI race game. The evolution of the training process, including competition outcomes (gold medal, tournament record), substantiates the effectiveness of this type of training. Most importantly, the present study provides multifaceted evidence on the efficacy of subject learning during BCI training. Learning correlates could be derived at all levels of the interface—application, BCI output, and electroencephalography—with two end-users, longitudinal evaluation, and, importantly, under real-world and even adverse conditions.

Real-time decoding of covert attention in higher-order visual areas

Brain-computer-interfaces (BCI) provide a means of using human brain activations to control devices for communication. Until now this has only been demonstrated in primary motor and sensory brain regions, using surgical implants or non-invasive neuroimaging techniques. Here, we provide proof-of-principle for the use of higher-order brain regions involved in complex cognitive processes such as attention. Using realtime fMRI, we implemented an online 'winner-takes-all approach' with quadrant-specific parameter estimates, to achieve single-block classification of brain activations. These were linked to the covert allocation of attention to real-world images presented at 4-quadrant locations. Accuracies in three target regions were significantly above chance, with individual decoding accuracies reaching upto 70%. By utilising higher order mental processes, 'cognitive BCIs' access varied and therefore more versatile information, potentially providing a platform for communication in patients who are unable to speak or move due to brain injury.

Changes in cortical network connectivity with long-term brain-machine interface exposure after chronic amputation

Studies on neural plasticity associated with brain–machine interface (BMI) exposure have primarily documented changes in single neuron activity, and largely in intact subjects. Here, we demonstrate significant changes in ensemble-level functional connectivity among primary motor cortical (MI) neurons of chronically amputated monkeys exposed to control a multiple-degree-of-freedom robot arm. A multi-electrode array was implanted in M1 contralateral or ipsilateral to the amputation in three animals. Two clusters of stably recorded neurons were arbitrarily assigned to control reach and grasp movements, respectively. With exposure, network density increased in a nearly monotonic fashion in the contralateral monkeys, whereas the ipsilateral monkey pruned the existing network before re-forming a denser connectivity. Excitatory connections among neurons within a cluster were denser, whereas inhibitory connections were denser among neurons across the two clusters. These results indicate that cortical network connectivity can be modified with BMI learning, even among neurons that have been chronically de-efferented and de-afferented due to amputation.

Previous studies have shown short-term plasticity in single neurons or local field potentials during brain-machine interface (BMI) training. Here the authors report long-term changes in functional connectivity of motor cortex neuronal ensemble activity as chronically amputated monkeys learn to operate a BMI.

Parsing learning in networks using brain-machine interfaces

Brain-machine interfaces (BMIs) define new ways to interact with our environment and hold great promise for clinical therapies. Motor BMIs, for instance, re-route neural activity to control movements of a new effector and could restore movement to people with paralysis. Increasing experience shows that interfacing with the brain inevitably changes the brain. BMIs engage and depend on a wide array of innate learning mechanisms to produce meaningful behavior. BMIs precisely define the information streams into and out of the brain, but engage wide-spread learning. We take a network perspective and review existing observations of learning in motor BMIs to show that BMIs engage multiple learning mechanisms distributed across neural networks. Recent studies demonstrate the advantages of BMI for parsing this learning and its underlying neural mechanisms. BMIs therefore provide a powerful tool for studying the neural mechanisms of learning that highlights the critical role of learning in engineered neural therapies.

Physiological properties of brain-machine interface input signals

Brain-machine interfaces (BMIs), also called brain-computer interfaces (BCIs), decode neural signals and use them to control some type of external device. Despite many experimental successes and terrific demonstrations in animals and humans, a high-performance, clinically viable device has not yet been developed for widespread usage. There are many factors that impact clinical viability and BMI performance. Arguably, the first of these is the selection of brain signals used to control BMIs. In this review, we summarize the physiological characteristics and performance-including movement-related information, longevity, and stability-of multiple types of input signals that have been used in invasive BMIs to date. These include intracortical spikes as well as field potentials obtained inside the cortex, at the surface of the cortex (electrocorticography), and at the surface of the dura mater (epidural signals). We also discuss the potential for future enhancements in input signal performance, both by improving hardware and by leveraging the knowledge of the physiological characteristics of these signals to improve decoding and stability.

Intracortical Brain-Machine Interfaces Advance Sensorimotor Neuroscience

Brain-machine interfaces (BMIs) decode brain activity to control external devices. Over the past two decades, the BMI community has grown tremendously and reached some impressive milestones, including the first human clinical trials using chronically implanted intracortical electrodes. It has also contributed experimental paradigms and important findings to basic neuroscience. In this review, we discuss neuroscience achievements stemming from BMI research, specifically that based upon upper limb prosthetic control with intracortical microelectrodes. We will focus on three main areas: first, we discuss progress in neural coding of reaches in motor cortex, describing recent results linking high dimensional representations of cortical activity to muscle activation. Next, we describe recent findings on learning and plasticity in motor cortex on various time scales. Finally, we discuss how bidirectional BMIs have led to better understanding of somatosensation in and related to motor cortex.

Design and Optimization of an EEG-Based Brain Machine Interface (BMI) to an Upper-Limb Exoskeleton for Stroke Survivors

This study demonstrates the feasibility of detecting motor intent from brain activity of chronic stroke patients using an asynchronous electroencephalography (EEG)-based brain machine interface (BMI). Intent was inferred from movement related cortical potentials (MRCPs) measured over an optimized set of EEG electrodes. Successful intent detection triggered the motion of an upper-limb exoskeleton (MAHI Exo-II), to guide movement and to encourage active user participation by providing instantaneous sensory feedback. Several BMI design features were optimized to increase system performance in the presence of single-trial variability of MRCPs in the injured brain: (1) an adaptive time window was used for extracting features during BMI calibration; (2) training data from two consecutive days were pooled for BMI calibration to increase robustness to handle the day-to-day variations typical of EEG, and (3) BMI predictions were gated by residual electromyography (EMG) activity from the impaired arm, to reduce the number of false positives. This patient-specific BMI calibration approach can accommodate a broad spectrum of stroke patients with diverse motor capabilities. Following BMI optimization on day 3, testing of the closed-loop BMI-MAHI exoskeleton, on 4th and 5th days of the study, showed consistent BMI performance with overall mean true positive rate (TPR) = 62.7 ± 21.4% on day 4 and 67.1 ± 14.6% on day 5. The overall false positive rate (FPR) across subjects was 27.74 ± 37.46% on day 4 and 27.5 ± 35.64% on day 5; however for two subjects who had residual motor function and could benefit from the EMG-gated BMI, the mean FPR was quite low (< 10%). On average, motor intent was detected -367 ± 328 ms before movement onset during closed-loop operation. These findings provide evidence that closed-loop EEG-based BMI for stroke patients can be designed and optimized to perform well across multiple days without system recalibration.

Brain-computer interfaces for dissecting cognitive processes underlying sensorimotor control

Sensorimotor control engages cognitive processes such as prediction, learning, and multisensory integration. Understanding the neural mechanisms underlying these cognitive processes with arm reaching is challenging because we currently record only a fraction of the relevant neurons, the arm has nonlinear dynamics, and multiple modalities of sensory feedback contribute to control. A brain-computer interface (BCI) is a well-defined sensorimotor loop with key simplifying advantages that address each of these challenges, while engaging similar cognitive processes. As a result, BCI is becoming recognized as a powerful tool for basic scientific studies of sensorimotor control. Here, we describe the benefits of BCI for basic scientific inquiries and review recent BCI studies that have uncovered new insights into the neural mechanisms underlying sensorimotor control.

Patient-specific cortical electrodes for sulcal and gyral implantation

PURPOSE

Noninvasive localization of certain brain functions may be mapped on a millimetre level. However, the interelectrode spacing of common clinical brain surface electrodes still remains around 10 mm. Here, we present details on development of electrodes for attaining higher quality electrocorticographic signals for use in functional brain mapping and brain-machine interface (BMI) technologies.

METHODS

We used platinum-plate-electrodes of 1-mm diameter to produce sheet electrodes after the creation of individualized molds using a 3-D printer and a press system that sandwiched the electrodes between personalized silicone sheets.

RESULTS

We created arrays to fit the surface curvature of the brain and inside the central sulcus, with interelectrode distances of 2.5 mm (a density of 16 times previous standard types). Rat experiments undertaken indicated no long term toxicity. We were also able to custom design, rapidly manufacture, safely implant, and confirm the efficacy of personalized electrodes, including the capability to attain meaningful high-gamma-band information in an amyotrophic lateral sclerosis patient.

CONCLUSION

We developed cortical sheet electrodes with a high-spatial resolution, tailor-made to match an individual's brain.

SIGNIFICANCE

This sheet electrode may contribute to the higher performance of BMI's.

Voluntary control of corticomuscular coherence through neurofeedback: a proof-of-principle study in healthy subjects

Corticomuscular coherence (CMC) relates to synchronization between activity in the motor cortex and the muscle activity. The strength of CMC can be affected by motor behavior. In a proof-of-principle study, we examined whether independent of motor output parameters, healthy subjects are able to voluntarily modulate CMC in a neurofeedback paradigm. Subjects received visual online feedback of their instantaneous CMC strength, which was calculated between an optimized spatial projection of multichannel electroencephalography (EEG) and electromyography (EMG) in an individually defined target frequency range. The neurofeedback training consisted of either increasing or decreasing CMC strength using a self-chosen mental strategy while performing a simple motor task. Evaluation of instantaneous coherence showed that CMC strength was significantly larger when subjects had to increase than when to decrease CMC; this difference between the two task conditions did not depend on motor performance. The exclusion of confounding factors such as motor performance, attention and task complexity in study design provides evidence that subjects were able to voluntarily modify CMC independent of motor output parameters. Additional analysis further strengthened the assumption that the subjects' response was specifically shaped by the neurofeedback. In perspective, we suggest that CMC-based neurofeedback could provide a therapeutic approach in clinical conditions, such as motor stroke, where CMC is altered.

Combining decoder design and neural adaptation in brain-machine interfaces

Brain-machine interfaces (BMIs) aim to help people with paralysis by decoding movement-related neural signals into control signals for guiding computer cursors, prosthetic arms, and other assistive devices. Despite compelling laboratory experiments and ongoing FDA pilot clinical trials, system performance, robustness, and generalization remain challenges. We provide a perspective on how two complementary lines of investigation, that have focused on decoder design and neural adaptation largely separately, could be brought together to advance BMIs. This BMI paradigm should also yield new scientific insights into the function and dysfunction of the nervous system.

Identifying Engineering, Clinical and Patient's Metrics for Evaluating and Quantifying Performance of Brain-Machine Interface (BMI) Systems

Brain-machine interface (BMI) devices have unparalleled potential to restore functional movement capabilities to stroke, paralyzed and amputee patients. Although BMI systems have achieved success in a handful of investigative studies, translation of closed-loop neuroprosthetic devices from the laboratory to the market is challenged by gaps in the scientific data regarding long-term device reliability and safety, uncertainty in the regulatory, market and reimbursement pathways, lack of metrics for evaluating and quantifying performance in BMI systems, as well as patient-acceptance challenges that impede their fast and effective translation to the end user. This review focuses on the identification of engineering, clinical and user's BMI metrics for new and existing BMI applications.

The role of muscle synergies in myoelectric control: trends and challenges for simultaneous multifunction control

Myoelectric control is filled with potential to significantly change human-robot interaction due to the ability to non-invasively measure human motion intent. However, current control schemes have struggled to achieve the robust performance that is necessary for use in commercial applications. As demands in myoelectric control trend toward simultaneous multifunctional control, multi-muscle coordinations, or synergies, play larger roles in the success of the control scheme. Detecting and refining patterns in muscle activations robust to the high variance and transient changes associated with surface electromyography is essential for efficient, user-friendly control. This article reviews the role of muscle synergies in myoelectric control schemes by dissecting each component of the scheme with respect to associated challenges for achieving robust simultaneous control of myoelectric interfaces. Electromyography recording details, signal feature extraction, pattern recognition and motor learning based control schemes are considered, and future directions are proposed as steps toward fulfilling the potential of myoelectric control in clinically and commercially viable applications.

Closed-loop brain-machine-body interfaces for noninvasive rehabilitation of movement disorders

Traditional approaches for neurological rehabilitation of patients affected with movement disorders, such as Parkinson's disease (PD), dystonia, and essential tremor (ET) consist mainly of oral medication, physical therapy, and botulinum toxin injections. Recently, the more invasive method of deep brain stimulation (DBS) showed significant improvement of the physical symptoms associated with these disorders. In the past several years, the adoption of feedback control theory helped DBS protocols to take into account the progressive and dynamic nature of these neurological movement disorders that had largely been ignored so far. As a result, a more efficient and effective management of PD cardinal symptoms has emerged. In this paper, we review closed-loop systems for rehabilitation of movement disorders, focusing on PD, for which several invasive and noninvasive methods have been developed during the last decade, reducing the complications and side effects associated with traditional rehabilitation approaches and paving the way for tailored individual therapeutics. We then present a novel, transformative, noninvasive closed-loop framework based on force neurofeedback and discuss several future developments of closed-loop systems that might bring us closer to individualized solutions for neurological rehabilitation of movement disorders.

Decoding the individual finger movements from single-trial functional magnetic resonance imaging recordings of human brain activity

Multivariate pattern classification analysis (MVPA) has been applied to functional magnetic resonance imaging (fMRI) data to decode brain states from spatially distributed activation patterns. Decoding upper limb movements from non-invasively recorded human brain activation is crucial for implementing a brain-machine interface that directly harnesses an individual's thoughts to control external devices or computers. The aim of this study was to decode the individual finger movements from fMRI single-trial data. Thirteen healthy human subjects participated in a visually cued delayed finger movement task, and only one slight button press was performed in each trial. Using MVPA, the decoding accuracy (DA) was computed separately for the different motor-related regions of interest. For the construction of feature vectors, the feature vectors from two successive volumes in the image series for a trial were concatenated. With these spatial-temporal feature vectors, we obtained a 63.1% average DA (84.7% for the best subject) for the contralateral primary somatosensory cortex and a 46.0% average DA (71.0% for the best subject) for the contralateral primary motor cortex; both of these values were significantly above the chance level (20%). In addition, we implemented searchlight MVPA to search for informative regions in an unbiased manner across the whole brain. Furthermore, by applying searchlight MVPA to each volume of a trial, we visually demonstrated the information for decoding, both spatially and temporally. The results suggest that the non-invasive fMRI technique may provide informative features for decoding individual finger movements and the potential of developing an fMRI-based brain-machine interface for finger movement.