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BACKGROUND: Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease that causes eventual death through respiratory failure unless mechanical ventilation is provided. Brain-machine interfaces (BMIs) may provide brain control supports for communication and motor function. We investigated the interests and expectations of patients with ALS concerning BMIs based on a large-scale anonymous questionnaire survey supported by the Japan Amyotrophic Lateral Sclerosis Association. METHODS: We surveyed 1918 patients with ALS regarding their present status, tracheostomy use, interest in BMIs, and their level of expectation for communication (conversation, emergency alarm, internet, and writing letters) and movement support (postural change, controlling the bed, controlling household appliances, robotic arms, and wheel chairs). FINDINGS: Seven hundred and eighty participants responded. Fifty-eight percent of the participants underwent tracheostomy. Approximately, 80% of the patients experienced stress or trouble during communication. For all nine supports, > 60% participants expressed expectations regarding BMIs. More than 98% of participants who underwent tracheostomy expected support with conversation and emergency alarms. Participants who did not undergo tracheostomy exhibited significantly greater expectations than participants with tracheostomy did regarding all five movement supports. Seventy-seven percent of participants were interested in BMIs. Participants aged < 60 years had greater interest in both BMIs. INTERPRETATION: This is the first large-scale survey to reveal the present status of patients with ALS and probe their interests and expectations regarding BMIs. Communication and emergency alarms should be supported by BMIs initially. BMIs should provide wide-ranging and high-performance support that can easily be used by severely disabled elderly patients with ALS.

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Brain signals recorded from the primary motor cortex (M1) are known to serve a significant role in coding the information brain-machine interfaces (BMIs) need to perform real and imagined movements, and also to form several functional networks with motor association areas. However, whether functional networks between M1 and other brain regions, such as these motor association areas, are related to the performance of BMIs is unclear. To examine the relationship between functional connectivity and performance of BMIs, we analyzed the correlation coefficient between performance of neural decoding and functional connectivity over the whole brain using magnetoencephalography. Ten healthy participants were instructed to execute or imagine three simple right upper limb movements. To decode the movement type, we extracted 40 virtual channels in the left M1 via the beam forming approach, and used them as a decoding feature. In addition, seed-based functional connectivities of activities in the alpha band during real and imagined movements were calculated using imaginary coherence. Seed voxels were set as the same virtual channels in M1. After calculating the imaginary coherence in individuals, the correlation coefficient between decoding accuracy and strength of imaginary coherence was calculated over the whole brain. The significant correlations were distributed mainly to motor association areas for both real and imagined movements. These regions largely overlapped with brain regions that had significant connectivity to M1. Our results suggest that use of the strength of functional connectivity between M1 and motor association areas has the potential to improve the performance of BMIs to perform real and imagined movements.

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A brain machine interface (BMI) provides the possibility of controlling such external devices as prosthetic arms for patients with severe motor dysfunction using their own brain signals. However, there have been few studies investigating the decoding accuracy for multiclasses of useful unilateral upper limb movements using non-invasive measurements. We investigated the decoding accuracy for classifying three types of unilateral upper limb movements using single-trial magnetoencephalography (MEG) signals. Neuromagnetic activities were recorded in 9 healthy subjects performing 3 types of right upper limb movements: hand grasping, pinching, and elbow flexion. A support vector machine was used to classify the single-trial MEG signals. The movement types were predicted with an average accuracy of 66 ± 10% (chance level: 33.3%) using neuromagnetic activity during a 400-ms interval (-200 ms to 200 ms from movement onsets). To explore the time-dependency of the decoding accuracy, we also examined the time course of decoding accuracy in 50-ms sliding windows from -500 ms to 500 ms. Decoding accuracies significantly increased and peaked once before (50.1 ± 4.9%) and twice after (58.5 ± 7.5% and 64.4 ± 7.6%) movement onsets in all subjects. Significant variability in the decoding features in the first peak was evident in the channels over the parietal area and in the second and third peaks in the channels over the sensorimotor area. Our results indicate that the three types of unilateral upper limb movement can be inferred with high accuracy by detecting differences in movement-related brain activity in the parietal and sensorimotor areas.

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In order to clarify whether neurophysiological profiles affect the performance of brain machine interfaces (BMI), we examined the relationships between amplitudes of movement-related cortical fields (MRCFs) and decoding performances during movement. Neuromagnetic activities were recorded in nine healthy participants during three types of unilateral upper limb movements. The movement types were inferred by a support vector machine. The amplitude of MRCF components, motor field (MF), movement-evoked field I (MEFI), and movement-evoked field II (MEFII) were compared with the decoding accuracies in all participants. Decoding accuracies at the latencies of MF, MEFI, and MEFII surpassed the chance level in all participants. In particular, accuracies at MEFI and MEFII were significantly higher in comparison with that of MF. The amplitudes and decoding accuracies were strongly correlated (MF, r(s)=0.90; MEFI, r(s)=0.90; and MEFII, r(s)=0.87). Our results show that the variation of MRCF components among participants reflects decoding performance. Neurophysiological profiles may serve as a predictor of individual BMI performance and assist in the improvement of general BMI performance.

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The frequency profiles and time courses of oscillatory changes when reading words are not fully understood, although there have been many reports that oscillatory dynamics reflect local brain function. In order to clarify oscillatory dynamics, we investigated the frequency and spatiotemporal distributions of neuromagnetic activities during silent reading of words in 23 healthy subjects. Individual data were divided into the following frequency bands: theta (5-8 Hz), alpha (8-13 Hz), beta (13-25 Hz), low gamma (25-50 Hz), and high gamma (50-100 Hz), and were analyzed by synthetic aperture magnetometry (SAM). The time window was consecutively moved in steps of 50 ms. Group analysis was performed to delineate common areas of brain activation. A transient power increase in the theta band occurred first in the bilateral occipital cortices, and then rapidly propagated to the left temporo-occipital areas, left inferior and middle frontal gyri, bilateral medial prefrontal cortices, and finally to the left anterior temporal cortices, which possibly reflects a serial cognitive process. This serial propagation of the transient power increase in the theta band was followed by sustained power decreases in the alpha, beta and low gamma bands. These results suggest that the transient power increase in the theta bands may be associated with priming and propagation of local activities, while sustained power decreases in the alpha, beta and low gamma bands reflect parallel neural processes related to silent reading words. Our results showed a relationship between frequency bands of oscillatory changes and locations. This may have implications in the relationship between frequency bands and functions.

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