update

.gitignore

@@ -4,8 +4,9 @@ __pycache__/
 # Distribution / packaging
 build/
 dist/
-
+upperHost_stim/
-# Environments
+!upperHost_stim/MI_headless.py
+!upperHost_stim/ssmvep_headless.py
 .env
 .venv
 env/

Decoder.py

@@ -231,7 +231,7 @@ class Decoder_main(threading.Thread):
         if self.zmqServer.open_Impedance:  # 阻抗检测状态不解码
             return
         data = self.zmqServer.paradigmBuffer.getDataViaSSVEP(50)
-        algo_log(f"SSVEP取出的：{data.shape}, data = {data[:20]}", level="DEBUG")
+        # algo_log(f"SSVEP取出的：{data.shape}, data = {data[:20]}", level="DEBUG")
         data = data[:self.n_chan, :]
         if self.decodingSteps == 1 and hasattr(self,'dw'):  # 开始预热
             self.dw.onlineInit()  # 刺激闪烁的第1s重置 --在线数据采集时
@@ -254,7 +254,7 @@ class Decoder_main(threading.Thread):
     def decoder_SSMVEP(self):
         '''模型训练'''
         if self.load_model == False and all(
-                self.trainLabel.count(i) >= self.single_train for i in range(len(self.list_freqs))):  # 模型尚未训练完成
+                self.trainLabel.count(i) >= self.single_train for i in [1, 2]):  # 模型尚未训练完成
             self.trainData = np.array(self.trainData)
             self.trainLabel = np.array(self.trainLabel)
             algo_log(f"开始SSMVEP模型训练，数据形状：{np.shape(self.trainData)}，标签形状：{self.trainLabel.shape}", level="DEBUG")
@@ -301,6 +301,7 @@ class Decoder_main(threading.Thread):
                 if self.zmqServer.epoch_finished == False or self.zmqServer.paradigmBuffer.GetDataLenCount() < \
                         self.interval_epoch[1] \
                         + self.zmqServer.event_inner_idx:
+                    # algo_log(f"SSMVEP模型启动预测  {self.zmqServer.epoch_finished}", level="DEBUG")
                     time.sleep(0.0001)
                     return
                 data = self.zmqServer.paradigmBuffer.get_SSMVEPData()  # 读取全部数据
@@ -327,7 +328,7 @@ class Decoder_main(threading.Thread):
     def decoder_MI(self):
         '''模型训练'''
         if self.train_started == False and all(
-                self.trainLabel.count(i) >= self.single_train for i in range(self.num_target)):  # 模型尚未训练
+                self.trainLabel.count(i) >= self.single_train for i in [1, 2]):  # 模型尚未训练
             self.zmqServer.broadcast_message('paradigm', 2)  # 模型训练前，训练集采集完毕，通知上位机
             self.train_started = True
             self.trainData = np.array(self.trainData)
@@ -370,6 +371,7 @@ class Decoder_main(threading.Thread):
         if self.zmqServer.state_mode == 'train' and self.train_started == False:  # 训练状态
             if self.zmqServer.epoch_finished and self.zmqServer.paradigmBuffer.GetDataLenCount() >= \
                     self.interval_epoch[1] + self.zmqServer.event_inner_idx:
+                self.currentLabel = self.zmqServer.currentLabel  # 同步当前标签
                 algo_log(f"训练队列数据：{self.zmqServer.paradigmBuffer.GetDataLenCount()}", level="DEBUG")
                 originalTrial = self.zmqServer.paradigmBuffer.get_MIData()  # 取出MI导联数据
                 algo_log(f"取出的：{originalTrial.shape},event: {originalTrial[-2, self.zmqServer.event_inner_idx]}", level="DEBUG")

config.ini

@@ -15,6 +15,8 @@ Audio_device = 0
 Rest_time = 2
 Upper_Host = 127.0.0.1
 Upper_Port = 8088
+Decoder_Host = 127.0.0.1
+Decoder_Port = 8099
 Serial_port = COM44
 algo_log_level = DEBUG
 console_output = 1

datamock.py

@@ -11,8 +11,8 @@ N_CHAN = 66           # 通道数: 64 EEG + 1 标签值 + 1 标签序号
 EEG_FREQ = 10         # EEG 正弦波频率 Hz
 EEG_AMP = 100.0      # EEG 幅值 100μV
 LABEL_INTERVAL = 5    # 标签间隔秒数
-# SERVER_ADDR = 'tcp://127.0.0.1:8100'
+SERVER_ADDR = 'tcp://127.0.0.1:8100'
-SERVER_ADDR = 'tcp://10.200.27.140:8100'
+LABEL_CMD_ADDR = 'tcp://127.0.0.1:8101'  # 接收来自上位机范式的标签命令
 
 # 发送间隔: 每包 5 采样点 / 250Hz = 20ms
 PKT_INTERVAL = N_SAMPLES_PER_PKT / FS
@@ -67,9 +67,41 @@ def main():
     sock.connect(SERVER_ADDR)
     print(f"[{datetime.now().strftime('%H:%M:%S')}] ZMQ Dealer 连接到 {SERVER_ADDR}")
 
+    # ========== 上位机标签命令监听 ==========
+    # 使用线程安全的队列接收来自 ssmvep_main.py 的标签命令
+    # 标签值: 1 (train 0), 2 (train 1), 99 (predict)
+    pending_label = [None]       # [label_value or None]
+    label_lock = threading.Lock()
+
+    label_cmd_sock = ctx.socket(zmq.PULL)
+    label_cmd_sock.bind(LABEL_CMD_ADDR)
+    print(f"[{datetime.now().strftime('%H:%M:%S')}] 标签命令监听绑定到 {LABEL_CMD_ADDR}")
+
+    stop_recv = threading.Event()
+
+    def label_cmd_thread():
+        """监听来自上位机范式的标签命令，写入 pending_label"""
+        while not stop_recv.is_set():
+            try:
+                msg = label_cmd_sock.recv_string(zmq.NOBLOCK)
+                label_val = int(msg)
+                with label_lock:
+                    pending_label[0] = label_val
+                ts = datetime.now().strftime('%H:%M:%S')
+                label_name = {1: 'train_0', 2: 'train_1', 99: 'predict'}.get(label_val, str(label_val))
+                print(f"[{ts}] 收到标签命令: {label_name} -> label={label_val}")
+            except zmq.Again:
+                time.sleep(0.005)
+            except Exception as e:
+                print(f"[label_cmd_thread] 错误: {e}")
+                time.sleep(0.01)
+
+    label_thread = threading.Thread(target=label_cmd_thread, daemon=True)
+    label_thread.start()
+    print(f"[{datetime.now().strftime('%H:%M:%S')}] 标签命令监听线程已启动")
+
     # 后台消费线程：持续 recv 从 ROUTER 返回的数据，避免 server 发送队列积压
     recv_count = [0]
-    stop_recv = threading.Event()
 
     def consumer_thread():
         """消费线程：阻塞 recv，丢弃收到的数据，仅用于清空 ROUTER 发送队列"""
@@ -98,7 +130,7 @@ def main():
     print(f"[{datetime.now().strftime('%H:%M:%S')}] 开始发送模拟数据 ...")
     print(f"  采样率: {FS}Hz | 每包 {N_SAMPLES_PER_PKT} 采样点 | 发送间隔 {PKT_INTERVAL*1000:.0f}ms")
     print(f"  EEG: {EEG_FREQ}Hz 正弦波 | 幅值 {EEG_AMP}μV")
-    print(f"  标签: 每 {LABEL_INTERVAL}s 末尾采样点触发 | label 1/2 交替")
+    print(f"  标签: 来自上位机范式命令 (train_0=1, train_1=2, predict=99)")
     print("-" * 50)
 
     try:
@@ -108,30 +140,21 @@ def main():
             # 构建当前包
             packet = build_packet(global_sample_idx)
 
-            # 检查是否需要放置标签
+            # 检查是否有来自上位机范式的挂起标签命令
-            if should_send_label(global_sample_idx):
+            with label_lock:
-                if label_type == 1:
+                ext_label = pending_label[0]
-                    label1_count += 1
+                if ext_label is not None:
-                    label_value = 1
+                    pending_label[0] = None
-                    label_number = label1_count
-                else:
-                    label2_count += 1
-                    label_value = 2
-                    label_number = label2_count
-
-                # 标签放在当前包最后一个采样点（索引 4）
-                packet[4, 64] = label_value
-                packet[4, 65] = label_number
 
+            if ext_label is not None:
+                # 将标签写入当前包所有5个采样点的第65通道 (index 64)
+                # 覆盖全部采样点确保 event_inner_idx 无论落在哪个位置都能被正确检测
+                packet[:, 64] = float(ext_label)
                 ts = datetime.now().strftime('%H:%M:%S')
-                print(f"[{ts}] 标签触发: label={label_value}, 序号={label_number} "
+                print(f"[{ts}] 打标签: label={ext_label} -> ch64[all 5 samples] (global_sample_idx={global_sample_idx})")
-                      f"(global_sample_idx={global_sample_idx})")
 
-                # 交替标签类型
+            # 发送: multipart 2帧 ['', data]
-                label_type = 2 if label_type == 1 else 1
+            # 使用标准格式，ROUTER 会自动附加 ZMQ 分配的客户端身份
-
-            # 发送: multipart 3帧 [identity, '', data]
-            # 使用标准格式（3帧），ROUTER 会自动附加 ZMQ 分配的客户端身份
             sock.send_multipart([
                 b'',
                 packet.tobytes()
@@ -156,6 +179,7 @@ def main():
     finally:
         stop_recv.set()
         consumer.join(timeout=2)
+        label_cmd_sock.close()
         sock.close()
         ctx.term()
 

verify_datamock.py

@@ -0,0 +1,304 @@
+"""
+datamock 验证脚本（模拟算法端）
+作为 ZMQ ROUTER 监听 8100 端口，等待 datamock.py 连接并验证数据流
+
+运行顺序:
+  第一步: python verify_datamock.py   (先启动，监听 8100)
+  第二步: python datamock.py          (后启动，连接 8100)
+"""
+import zmq
+import numpy as np
+import time
+import sys
+import matplotlib
+matplotlib.use('TkAgg')
+
+# 在导入 pyplot 之前确保 Tkinter 正确初始化
+try:
+    import tkinter as tk
+    root = tk.Tk()
+    root.withdraw()  # 隐藏主窗口，我们只需要它的事件循环
+except Exception as e:
+    print(f"[WARN] Tkinter 初始化警告: {e}")
+
+import matplotlib.pyplot as plt
+from datetime import datetime
+
+# ===== 可视化参数 =====
+PLOT_WINDOW_SEC = 2.0      # 滑动窗口时长（秒）
+PLOT_CHANNELS = [0, 1, 2, 3]  # 要显示的 EEG 通道索引
+
+SERVER_ADDR = 'tcp://127.0.0.1:8100'
+FS = 250
+N_SAMPLES_PER_PKT = 5
+N_CHAN = 66
+EEG_FREQ = 10
+EEG_AMP = 100.0        # EEG 幅值 100μV（峰值）
+EEG_AMP_MEAN = EEG_AMP * 2 / np.pi  # 正弦波 |mean| ≈ 63.7μV
+EEG_AMP_TOLERANCE = 1.5             # 幅值容差倍数
+LABEL_INTERVAL = 5
+FFT_SAMPLES = 250           # 做一次 FFT 需要的采样点数（1s数据）
+EXPECTED_BYTES = N_SAMPLES_PER_PKT * N_CHAN * 4  # 1320 bytes (5*66*4)
+
+
+def validate_fft(samples):
+    """对 Ch0 数据做 FFT，返回峰值频率"""
+    freqs = np.fft.rfftfreq(FFT_SAMPLES, d=1 / FS)
+    fft_mag = np.abs(np.fft.rfft(samples))
+    peak_idx = np.argmax(fft_mag[1:]) + 1  # 跳过 DC
+    return freqs[peak_idx], fft_mag, freqs
+
+
+def main():
+    ctx = zmq.Context()
+    sock = ctx.socket(zmq.ROUTER)
+    sock.bind(SERVER_ADDR)
+    print(f"[{datetime.now().strftime('%H:%M:%S')}] ZMQ ROUTER 绑定 {SERVER_ADDR}，等待 datamock.py 连接...\n")
+
+    # ===== 初始化交互式绘图 =====
+    plt.ion()  # 开启交互模式
+    fig = plt.figure(figsize=(14, 10))
+    fig.suptitle('EEG Data Monitor (Real-time)', fontsize=14)
+
+    # 使用 GridSpec 进行布局
+    from matplotlib.gridspec import GridSpec
+    gs = GridSpec(len(PLOT_CHANNELS) + 2, 1, figure=fig, hspace=0.3)
+    axes = []
+    lines_eeg = []
+    for i, ch in enumerate(PLOT_CHANNELS):
+        ax = fig.add_subplot(gs[i])
+        axes.append(ax)
+        ax.set_ylabel(f'Ch{ch} (μV)', fontsize=8)
+        ax.grid(True, alpha=0.3)
+        ax.set_ylim(-150, 150)
+        line, = ax.plot([], [], lw=0.8)
+        lines_eeg.append(line)
+        ax.set_title(f'EEG Channel {ch}', fontsize=9)
+
+    # 标签通道子图 (Ch64 - 标签值)
+    ax_label = fig.add_subplot(gs[len(PLOT_CHANNELS)])
+    axes.append(ax_label)
+    ax_label.set_ylabel('Label Value', fontsize=8)
+    ax_label.grid(True, alpha=0.3)
+    ax_label.set_ylim(-0.5, 2.5)
+    line_label, = ax_label.plot([], [], 'ro-', lw=1.5, markersize=4)
+    line_label_data = line_label
+    ax_label.set_title('Ch64 - Label Value', fontsize=9)
+
+    # Ch65 标签序号子图
+    ax_seq = fig.add_subplot(gs[len(PLOT_CHANNELS) + 1])
+    axes.append(ax_seq)
+    ax_seq.set_ylabel('Label Seq', fontsize=8)
+    ax_seq.set_xlabel('Time (samples)', fontsize=8)
+    ax_seq.grid(True, alpha=0.3)
+    ax_seq.set_ylim(-0.5, 10)
+    line_seq, = ax_seq.plot([], [], 'gs-', lw=1.5, markersize=4)
+    line_seq_data = line_seq
+    ax_seq.set_title('Ch65 - Label Sequence', fontsize=9)
+
+    plt.tight_layout()
+
+    # ===== 状态 =====
+    global_idx = 0          # 全局采样点索引
+    label_events = []       # 捕获的标签事件
+    start_time = None
+    fft_done = False
+    fft_buffer = []         # 暂存前 250 点做 FFT
+    ch64_zero_ok = True     # 验证 Ch64 非标签采样点均为 0
+    ch65_zero_ok = True     # 验证 Ch65 非标签采样点均为 0
+    label_pos_ok_all = True # 验证标签均在包内索引 4
+
+    # ===== 数据缓冲区 =====
+    max_samples = int(FS * PLOT_WINDOW_SEC)
+    eeg_buffer = {ch: np.zeros(max_samples) for ch in PLOT_CHANNELS}
+    label_buffer = np.zeros(max_samples)
+    seq_buffer = np.zeros(max_samples)
+    time_axis = np.arange(max_samples)
+
+    # ZMQ 收发统计
+    recv_count = 0
+
+    try:
+        # 首次 pause 用于显示窗口
+        plt.pause(0.5)
+        print(f"[INFO] 交互窗口已显示，如未看到请检查任务栏")
+
+        while True:
+            # ROUTER recv: prepended 一个 identity 帧
+            # datamock 发送 3帧 [b'datamock', b'', data_bytes]
+            # ROUTER 接收后变成 4帧 [router_identity, b'datamock', b'', data_bytes]
+            frames = sock.recv_multipart()
+            recv_count += 1
+            now = time.time()
+            if start_time is None:
+                start_time = now
+
+            # 帧格式: [router_identity, b'datamock', b'', data_bytes]
+            router_id = frames[0]   # ROUTER 添加的身份帧
+            identity = frames[1]     # 发送端的 identity
+            _empty = frames[2]      # 空帧
+            raw_data = frames[3]     # 实际数据字节
+
+            # 数据长度校验
+            if len(raw_data) != EXPECTED_BYTES:
+                print(f"[ERROR] 数据长度错误: 期望{EXPECTED_BYTES}字节, 实际{len(raw_data)}字节")
+                continue
+
+            # 解析为 [5, 66] float32 数组
+            packet = np.frombuffer(raw_data, dtype=np.float32).reshape(N_SAMPLES_PER_PKT, N_CHAN)
+
+            elapsed = now - start_time
+
+            # ===== 验证 1: 数据形状 =====
+            if recv_count == 1:
+                shape_ok = packet.shape == (N_SAMPLES_PER_PKT, N_CHAN)
+                print(f"[{'✓' if shape_ok else '✗'}] 数据形状: {packet.shape}  "
+                      f"(期望 [{N_SAMPLES_PER_PKT}, {N_CHAN}])")
+                if not shape_ok:
+                    print(f"    ✗ 形状不匹配，退出")
+                    break
+
+            # ===== 验证 2: EEG 幅值（首包） =====
+            if recv_count == 1:
+                eeg = packet[:, :64]
+                amp_mean = np.mean(np.abs(eeg))
+                amp_ok = amp_mean <= EEG_AMP_MEAN * EEG_AMP_TOLERANCE
+                print(f"[{'✓' if amp_ok else '✗'}] EEG 幅值: 均值={amp_mean:.2f}μV  "
+                      f"(期望 ~{EEG_AMP_MEAN:.2f}μV，峰值 ~{EEG_AMP:.2f}μV)")
+                if not amp_ok:
+                    print(f"    ✗ 幅值超出容差范围")
+
+            # ===== 验证 3: EEG 频率（首秒数据收集满后做 FFT） =====
+            fft_buffer.append(packet[:, 0].copy())  # 收集 Ch0
+
+            if not fft_done and len(fft_buffer) * N_SAMPLES_PER_PKT >= FFT_SAMPLES:
+                # 凑够 250 点，做 FFT
+                all_ch0 = np.concatenate(fft_buffer)[:FFT_SAMPLES]
+                peak_freq, fft_mag, freqs = validate_fft(all_ch0)
+                freq_ok = abs(peak_freq - EEG_FREQ) < 1.0
+
+                print(f"[{'✓' if freq_ok else '✗'}] EEG 频率: 峰值={peak_freq:.1f}Hz  "
+                      f"(期望 ~{EEG_FREQ}Hz)")
+                print(f"    FFT 幅度谱前 5 峰值:")
+                top5 = np.argsort(fft_mag[1:])[-5:][::-1] + 1
+                for rank, idx in enumerate(top5):
+                    print(f"      {rank+1}. {freqs[idx]:.1f}Hz  幅度={fft_mag[idx]:.1f}")
+                print()
+                fft_done = True
+
+            # ===== 验证 4: 标签通道（Ch64/Ch65） =====
+            ch64 = packet[:, 64]
+            ch65 = packet[:, 65]
+            ch64_nonzero = np.where(ch64 != 0)[0]
+            ch65_nonzero = np.where(ch65 != 0)[0]
+
+            # 检查非标签采样点是否全为 0
+            ch64_zeros = np.all(ch64[:4] == 0)
+            ch65_zeros = np.all(ch65[:4] == 0)
+            ch64_zero_ok = ch64_zero_ok and ch64_zeros
+            ch65_zero_ok = ch65_zero_ok and ch65_zeros
+
+            if len(ch64_nonzero) > 0:
+                pos_in_pkt = int(ch64_nonzero[0])
+                label_val = int(ch64[pos_in_pkt])
+                label_seq = int(ch65[pos_in_pkt])
+
+                pos_ok = (len(ch64_nonzero) == 1 and pos_in_pkt == 4)
+                label_pos_ok_all = label_pos_ok_all and pos_ok
+
+                elapsed_since_start = now - start_time
+                print(f"[✓] 标签触发 @ {elapsed_since_start:.1f}s  "
+                      f"(global_idx={global_idx}  包{recv_count})")
+                print(f"    Ch64 标签值: {label_val}  Ch65 序号: {label_seq}")
+                print(f"    包内位置: 采样点 {pos_in_pkt}/4  "
+                      f"({'✓' if pos_ok else '✗ 期望 4'})  "
+                      f"其余采样点 Ch64=0: {'✓' if ch64_zeros else '✗'}  "
+                      f"Ch65=0: {'✓' if ch65_zeros else '✗'}")
+                print()
+
+                label_events.append({
+                    'time': elapsed_since_start,
+                    'label': label_val,
+                    'seq': label_seq
+                })
+
+            global_idx += N_SAMPLES_PER_PKT
+
+            # ===== 更新绘图缓冲区 =====
+            for ch_idx, ch in enumerate(PLOT_CHANNELS):
+                eeg_buffer[ch] = np.roll(eeg_buffer[ch], -N_SAMPLES_PER_PKT)
+                eeg_buffer[ch][-N_SAMPLES_PER_PKT:] = packet[:, ch]
+
+            label_buffer = np.roll(label_buffer, -N_SAMPLES_PER_PKT)
+            label_buffer[-N_SAMPLES_PER_PKT:] = packet[:, 64]
+
+            seq_buffer = np.roll(seq_buffer, -N_SAMPLES_PER_PKT)
+            seq_buffer[-N_SAMPLES_PER_PKT:] = packet[:, 65]
+
+            # ===== 实时更新绘图 =====
+            for i, ch in enumerate(PLOT_CHANNELS):
+                lines_eeg[i].set_data(time_axis, eeg_buffer[ch])  # 数据已是 μV 单位
+            line_label_data.set_data(time_axis, label_buffer)
+            line_seq_data.set_data(time_axis, seq_buffer)
+
+            # 设置 x 轴范围
+            for ax in axes:
+                ax.set_xlim(0, max_samples)
+
+            # 刷新图形（交互模式）
+            fig.canvas.draw_idle()
+            plt.pause(0.001)
+
+    except KeyboardInterrupt:
+        print("\n" + "=" * 55)
+        print("  验证结果汇总")
+        print("=" * 55)
+        print(f"  运行时长: {time.time() - start_time:.1f}s")
+        print(f"  收到包数: {recv_count}")
+        print(f"  FFT 验证: {'✓ 已完成' if fft_done else '✗ 未完成（时长不足1s）'}")
+        print(f"  非标签采样点 Ch64=0: {'✓' if ch64_zero_ok else '✗'}")
+        print(f"  非标签采样点 Ch65=0: {'✓' if ch65_zero_ok else '✗'}")
+        print(f"  标签均在包内位置4: {'✓' if label_pos_ok_all else '✗'}")
+
+        if label_events:
+            print(f"\n  共捕获 {len(label_events)} 次标签事件:")
+            for i, ev in enumerate(label_events):
+                print(f"    {i+1}. t={ev['time']:.1f}s  label={ev['label']}  序号={ev['seq']}")
+
+            # 标签间隔
+            print(f"\n  标签间隔验证 (期望 ~{LABEL_INTERVAL}s):")
+            for i in range(1, len(label_events)):
+                dt = label_events[i]['time'] - label_events[i-1]['time']
+                ok = abs(dt - LABEL_INTERVAL) < 0.1
+                print(f"    {i}->{i+1}: {dt:.2f}s  {'✓' if ok else '✗'}")
+
+            # 标签交替
+            labels = [e['label'] for e in label_events]
+            alt_ok = all(labels[i] != labels[i+1] for i in range(len(labels) - 1))
+            print(f"\n  标签交替: {labels}  {'✓ 交替正确' if alt_ok else '✗ 交替错误'}")
+
+            # 序号
+            label1_seqs = [e['seq'] for e in label_events if e['label'] == 1]
+            label2_seqs = [e['seq'] for e in label_events if e['label'] == 2]
+            s1_ok = label1_seqs == list(range(1, len(label1_seqs) + 1))
+            s2_ok = label2_seqs == list(range(1, len(label2_seqs) + 1))
+            print(f"  label=1 序号: {label1_seqs}  {'✓' if s1_ok else '✗'}")
+            print(f"  label=2 序号: {label2_seqs}  {'✓' if s2_ok else '✗'}")
+        else:
+            print(f"\n  未捕获标签事件（运行时长不足 {LABEL_INTERVAL}s）")
+
+        print("=" * 55)
+
+    finally:
+        sock.close()
+        ctx.term()
+        plt.ioff()
+        plt.close('all')
+        try:
+            root.destroy()
+        except:
+            pass
+
+
+if __name__ == '__main__':
+    main()