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Table 1 The ten most-cited papers published in JNER in 2017 (from Web of Science, accessed 9-1-2019). W = paper involves a technology worn (or implanted). AI = paper involves techniques from artificial intelligence, such as pattern recognition or machine learning. S = paper involves synergistic application of an adjuvant technique to understand or enhance therapy

From: JNER at 15 years: analysis of the state of neuroengineering and rehabilitation

Li et al. 2017, A motion-classification strategy based on sEMG-EEG signal combination for upper-limb amputees, JNER 14:2 [11] (34 cites)W, AI
Wang et al. 2017, Interactive wearable systems for upper body rehabilitation: a systematic review, JNER 14:20 [12] (29 cites)W, AI
Wendelken et al. 2017, Restoration of motor control and proprioceptive and cutaneous sensation in humans with prior upper-limb amputation via multiple Utah Slanted Electrode Arrays (USEAs) implanted in residual peripheral arm nerves JNER 14:121 [13] (24 cites)W, AI
Calabro RS et al. 2017, The role of virtual reality in improving motor performance as revealed by EEG: a randomized clinical trial, JNER 14:53 [14] (22 cites)(W), S
Galle et al. 2017, Reducing the metabolic cost of walking with an ankle exoskeleton: interaction between actuation timing and power, JNER 14:35 [15] (21 cites)W
Parastarfeizabadi and Kouzani 2017, Advances in closed-loop deep brain stimulation devices, JNER 14:79 [16] (21 cites)W, S
Nam KY et al. 2017, Robot-assisted gait training (Lokomat) improves walking function and activity in people with spinal cord injury: a systematic review, JNER 14:24 [17] (19 cites)(W)
Elsner B et al. 2017, Transcranial direct current stimulation (tDCS) for improving capacity in activities and arm function after stroke: a network meta-analysis of randomised controlled trials, JNER 14:95 [18] (13 cites)W, S
Dellacasa Bellingegni et al. 2017, A1 NLR, MLP, SVM, and LDA: a comparative analysis on EMG data from people with trans-radial amputation, JNER 14:82 [19] (13 cites)W, AI
Nguyen H et al. 2017, Auto detection and segmentation of daily living activities during a Timed Up and Go task in people with Parkinson’s disease using multiple inertial sensors, JNER 14.26 [20] (13 cites)W, AI